From cf7758294fdeaeff8fcaef53bd25ce7c4e9d5503 Mon Sep 17 00:00:00 2001 From: ckarag Date: Thu, 25 Jul 2024 11:48:15 +0000 Subject: [PATCH] deploy: ckarag/academic-kickstart@b719b0be3563791ba9aa16406a1ac2d42b981f8a --- index.json | 2 +- index.xml | 8 ++++---- post/index.xml | 4 ++-- post/writing-technical-content/index.html | 2 +- rtmonitor/index.html | 2 +- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/index.json b/index.json index 7efe1f96..4ca77870 100644 --- a/index.json +++ b/index.json @@ -1 +1 @@ -[{"authors":null,"categories":null,"content":"Haris Karagiannakis is a PhD candidate currently working on developing Machine Learning methodologies to improve real-time monitoring of economic activity. Haris has studied both economics and finance at a postgraduate level. He holds an M.Sc. in Economics from the London School of Economics and Political Science and an M.Sc. in Financial Economics from the University of Cyprus, while he also obtained merit-based scholarships to fund both his undergraduate and postgraduate studies. He has also earned various awards and distinctions in regional and international competitions for his undergraduate and postgraduate theses. Prior joining KCL, he worked as a research officer (between 2010 and 2019), involved in applied cutting-edge quantitative research on account of organizations and companies. His research interests include macro-economic forecasting and factor investment strategies. His research is fully funded by the Qatar Centre for Global Banking \u0026amp; Finance (QCGBF) at KCL. Haris has also been teaching various MSc courses at KCL, including Intro to Big Data Analytics and Stats Software for Finance.\nDownload my resumé .\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"e577d600689cd6980485bd14a2fd1b76","permalink":"","publishdate":"0001-01-01T00:00:00Z","relpermalink":"","section":"authors","summary":"Haris Karagiannakis is a PhD candidate currently working on developing Machine Learning methodologies to improve real-time monitoring of economic activity. Haris has studied both economics and finance at a postgraduate level.","tags":null,"title":"Haris Karagiannakis","type":"authors"},{"authors":null,"categories":null,"content":"Generated by Wowchemy - the FREE, Hugo-based open source website builder trusted by 500,000+ sites.\nEasily build anything with blocks - no-code required!\nFrom landing pages, second brains, and courses to academic resumés, conferences, and tech blogs.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"92097bd738c772f382c3e53d60846459","permalink":"https://ckarag.github.io/home-unused/demo-hero/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/demo-hero/","section":"home-unused","summary":"Generated by Wowchemy - the FREE, Hugo-based open source website builder trusted by 500,000+ sites.\nEasily build anything with blocks - no-code required!\nFrom landing pages, second brains, and courses to academic resumés, conferences, and tech blogs.","tags":null,"title":"Hugo Academic Theme","type":"home-unused"},{"authors":null,"categories":null,"content":"👋 Welcome to the Academic Template The Wowchemy Academic Resumé Template for Hugo empowers you to create your job-winning online resumé and showcase your academic publications.\nCheck out the latest demo of what you’ll get in less than 10 minutes, or view the showcase.\nWowchemy makes it easy to create a beautiful website for free. Edit your site in Markdown, Jupyter, or RStudio (via Blogdown), generate it with Hugo, and deploy with GitHub or Netlify. Customize anything on your site with widgets, themes, and language packs.\n👉 Get Started 📚 View the documentation 💬 Chat with the Wowchemy community or Hugo community 🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy 💡 Request a feature or report a bug for Wowchemy ⬆️ Updating Wowchemy? View the Update Guide and Release Notes Crowd-funded open-source software To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.\n❤️ Click here to unlock rewards with sponsorship You’re looking at a Wowchemy widget This homepage section is an example of adding elements to the Blank widget.\nBackgrounds can be applied to any section. Here, the background option is set give a color gradient.\nTo remove this section, delete content/home/demo.md.\nGet inspired Check out the Markdown files which power the Academic Demo, or view the showcase.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"1905ee7535e0a4535c48727a4bc5d258","permalink":"https://ckarag.github.io/home-unused/demo-links/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/demo-links/","section":"home-unused","summary":"👋 Welcome to the Academic Template The Wowchemy Academic Resumé Template for Hugo empowers you to create your job-winning online resumé and showcase your academic publications.\nCheck out the latest demo of what you’ll get in less than 10 minutes, or view the showcase.","tags":null,"title":"Academic Template","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"d6682c06ff2f3dd0fc28f7e2c0702d07","permalink":"https://ckarag.github.io/home-unused/experience/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/experience/","section":"home-unused","summary":"","tags":null,"title":"Experience","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"9e909a8894fd21a2eff4b3e43238d81e","permalink":"https://ckarag.github.io/home-unused/accomplishments/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/accomplishments/","section":"home-unused","summary":"","tags":null,"title":"Accomplish\u0026shy;ments","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"0e643989bdefe366f2b5fddf949a36b6","permalink":"https://ckarag.github.io/home-unused/posts/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/posts/","section":"home-unused","summary":"","tags":null,"title":"Recent Posts","type":"home-unused"},{"authors":null,"categories":null,"content":" ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"6d5b97766bbd075b2dc107b374efc3bc","permalink":"https://ckarag.github.io/home-unused/gallery/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/gallery/","section":"home-unused","summary":" ","tags":null,"title":"Gallery","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"d927b251d3da15a737d1f66fb88d4504","permalink":"https://ckarag.github.io/home-unused/talks/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/talks/","section":"home-unused","summary":"","tags":null,"title":"Recent \u0026 Upcoming Talks","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"28f54f6e819207239a6024bbaa9d78de","permalink":"https://ckarag.github.io/home-unused/featured/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/featured/","section":"home-unused","summary":"","tags":null,"title":"Featured Publications","type":"home-unused"},{"authors":null,"categories":null,"content":" Quickly discover relevant content by filtering publications. ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"19cfbeefa99b41865496b68f2fb35bad","permalink":"https://ckarag.github.io/home-unused/publications/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/publications/","section":"home-unused","summary":" Quickly discover relevant content by filtering publications. ","tags":null,"title":"Recent Publications","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"657179738bed56748434d6ae76e8a647","permalink":"https://ckarag.github.io/home-unused/tags/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/tags/","section":"home-unused","summary":"","tags":null,"title":"Popular Topics","type":"home-unused"},{"authors":[],"categories":null,"content":" Click on the Slides button above to view the built-in slides feature. Slides can be added in a few ways:\nCreate slides using Wowchemy’s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes. Further event details, including page elements such as image galleries, can be added to the body of this page.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"a8edef490afe42206247b6ac05657af0","permalink":"https://ckarag.github.io/talk/example-talk/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/example-talk/","section":"event","summary":"An example talk using Wowchemy's Markdown slides feature.","tags":[],"title":"Example Talk","type":"event"},{"authors":["admin","吳恩達"],"categories":["Demo","教程"],"content":"import libr print(\u0026#39;hello\u0026#39;) Overview The Wowchemy website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more Get Started 👉 Create a new site 📚 Personalize your site 💬 Chat with the Wowchemy community or Hugo community 🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy 💡 Request a feature or report a bug for Wowchemy ⬆️ Updating Wowchemy? View the Update Tutorial and Release Notes Crowd-funded open-source software To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.\n❤️ Click here to become a sponsor and help support Wowchemy’s future ❤️ As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨\nEcosystem Hugo Academic CLI: Automatically import publications from BibTeX Inspiration Check out the latest demo of what you’ll get in less than 10 minutes, or view the showcase of personal, project, and business sites.\nFeatures Page builder - Create anything with widgets and elements Edit any type of content - Blog posts, publications, talks, slides, projects, and more! Create content in Markdown, Jupyter, or RStudio Plugin System - Fully customizable color and font themes Display Code and Math - Code highlighting and LaTeX math supported Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more! Beautiful Site - Simple and refreshing one page design Industry-Leading SEO - Help get your website found on search engines and social media Media Galleries - Display your images and videos with captions in a customizable gallery Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site Multi-language - 34+ language packs including English, 中文, and Português Multi-user - Each author gets their own profile page Privacy Pack - Assists with GDPR Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects One-Click Deployment - No servers. No databases. Only files. Themes Wowchemy and its templates come with automatic day (light) and night (dark) mode built-in. Alternatively, visitors can choose their preferred mode - click the moon icon in the top right of the Demo to see it in action! Day/night mode can also be disabled by the site admin in params.toml.\nChoose a stunning theme and font for your site. Themes are fully customizable.\nLicense Copyright 2016-present George Cushen.\nReleased under the MIT license.\n","date":1607817600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1607817600,"objectID":"279b9966ca9cf3121ce924dca452bb1c","permalink":"https://ckarag.github.io/post/getting-started/","publishdate":"2020-12-13T00:00:00Z","relpermalink":"/post/getting-started/","section":"post","summary":"Welcome 👋 We know that first impressions are important, so we've populated your new site with some initial content to help you get familiar with everything in no time.","tags":["Academic","开源"],"title":"Welcome to Wowchemy, the website builder for Hugo","type":"post"},{"authors":null,"categories":null,"content":"Visualizing the change in inflation dynamics as captured by LASSO using a large macro dataset (FRED-MD). Click here to see an animated (gif) version of the word clouds.\n","date":1583971200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1583971200,"objectID":"4a3d7ff33938b41d752e0123a79cafa7","permalink":"https://ckarag.github.io/project/inflationdyn/","publishdate":"2020-03-12T00:00:00Z","relpermalink":"/project/inflationdyn/","section":"project","summary":"Visualizing the change in inflation dynamics as captured by LASSO using a large macro dataset (FRED-MD). See {{\u003c staticref \"MLinterpret_CPI.html\" \"newtab\" \u003e}}here{{\u003c /staticref \u003e}} for an animated (gif) version.","tags":["Machine Learning","Forecasting"],"title":"US Inflation Dynamics","type":"project"},{"authors":null,"categories":null,"content":"Wowchemy is designed to give technical content creators a seamless experience. You can focus on the content and Wowchemy handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.\nOn this page, you’ll find some examples of the types of technical content that can be rendered with Wowchemy.\nExamples Code Wowchemy supports a Markdown extension for highlighting code syntax. You can customize the styles under the syntax_highlighter option in your config/_default/params.yaml file.\n```python import pandas as pd data = pd.read_csv(\u0026#34;data.csv\u0026#34;) data.head() ``` renders as\nimport pandas as pd data = pd.read_csv(\u0026#34;data.csv\u0026#34;) data.head() Mindmaps Wowchemy supports a Markdown extension for mindmaps.\nSimply insert a Markdown markmap code block and optionally set the height of the mindmap as shown in the example below.\nA simple mindmap defined as a Markdown list:\n```markmap {height=\u0026#34;200px\u0026#34;} - Hugo Modules - wowchemy - wowchemy-plugins-netlify - wowchemy-plugins-netlify-cms - wowchemy-plugins-reveal ``` renders as\n- Hugo Modules - wowchemy - wowchemy-plugins-netlify - wowchemy-plugins-netlify-cms - wowchemy-plugins-reveal A more advanced mindmap with formatting, code blocks, and math:\n```markmap - Mindmaps - Links - [Wowchemy Docs](https://wowchemy.com/docs/) - [Discord Community](https://discord.gg/z8wNYzb) - [GitHub](https://github.com/wowchemy/wowchemy-hugo-themes) - Features - Markdown formatting - **inline** ~~text~~ *styles* - multiline text - `inline code` - ```js console.log(\u0026#39;hello\u0026#39;); console.log(\u0026#39;code block\u0026#39;); ``` - Math: $x = {-b \\pm \\sqrt{b^2-4ac} \\over 2a}$ ``` renders as\n- Mindmaps - Links - [Wowchemy Docs](https://wowchemy.com/docs/) - [Discord Community](https://discord.gg/z8wNYzb) - [GitHub](https://github.com/wowchemy/wowchemy-hugo-themes) - Features - Markdown formatting - **inline** ~~text~~ *styles* - multiline text - `inline code` - ```js console.log(\u0026#39;hello\u0026#39;); console.log(\u0026#39;code block\u0026#39;); ``` - Math: $x = {-b \\pm \\sqrt{b^2-4ac} \\over 2a}$ Charts Wowchemy supports the popular Plotly format for interactive charts.\nSave your Plotly JSON in your page folder, for example line-chart.json, and then add the {{\u0026lt; chart data=\u0026#34;line-chart\u0026#34; \u0026gt;}} shortcode where you would like the chart to appear.\nDemo:\nYou might also find the Plotly JSON Editor useful.\nMath Wowchemy supports a Markdown extension for $\\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.\nTo render inline or block math, wrap your LaTeX math with {{\u0026lt; math \u0026gt;}}$...${{\u0026lt; /math \u0026gt;}} or {{\u0026lt; math \u0026gt;}}$$...$${{\u0026lt; /math \u0026gt;}}, respectively. (We wrap the LaTeX math in the Wowchemy math shortcode to prevent Hugo rendering our math as Markdown. The math shortcode is new in v5.5-dev.)\nExample math block:\n{{\u0026lt; math \u0026gt;}} $$ \\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |}{\\left \\|\\nabla F(\\mathbf{x}_{n}) - \\nabla F(\\mathbf{x}_{n-1}) \\right \\|^2} $$ {{\u0026lt; /math \u0026gt;}} renders as\n$$\\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |}{\\left \\|\\nabla F(\\mathbf{x}_{n}) - \\nabla F(\\mathbf{x}_{n-1}) \\right \\|^2}$$ Example inline math {{\u0026lt; math \u0026gt;}}$\\nabla F(\\mathbf{x}_{n})${{\u0026lt; /math \u0026gt;}} renders as $\\nabla F(\\mathbf{x}_{n})$.\nExample multi-line math using the math linebreak (\\\\):\n{{\u0026lt; math \u0026gt;}} $$f(k;p_{0}^{*}) = \\begin{cases}p_{0}^{*} \u0026amp; \\text{if }k=1, \\\\ 1-p_{0}^{*} \u0026amp; \\text{if }k=0.\\end{cases}$$ {{\u0026lt; /math \u0026gt;}} renders as\n$$ f(k;p_{0}^{*}) = \\begin{cases}p_{0}^{*} \u0026amp; \\text{if }k=1, \\\\ 1-p_{0}^{*} \u0026amp; \\text{if }k=0.\\end{cases} $$ Diagrams Wowchemy supports a Markdown extension for diagrams. You can enable this feature by toggling the diagram option in your config/_default/params.toml file or by adding diagram: true to your page front matter.\nAn example flowchart:\n```mermaid graph TD A[Hard] --\u0026gt;|Text| B(Round) B --\u0026gt; C{Decision} C --\u0026gt;|One| D[Result 1] C --\u0026gt;|Two| E[Result 2] ``` renders as\ngraph TD A[Hard] --\u0026gt;|Text| B(Round) B --\u0026gt; C{Decision} C --\u0026gt;|One| D[Result 1] C --\u0026gt;|Two| E[Result 2] An example sequence diagram:\n```mermaid sequenceDiagram Alice-\u0026gt;\u0026gt;John: Hello John, how are you? loop Healthcheck John-\u0026gt;\u0026gt;John: Fight against hypochondria end Note right of John: Rational thoughts! John--\u0026gt;\u0026gt;Alice: Great! John-\u0026gt;\u0026gt;Bob: How about you? Bob--\u0026gt;\u0026gt;John: Jolly good! ``` renders as\nsequenceDiagram Alice-\u0026gt;\u0026gt;John: Hello John, how are you? loop Healthcheck John-\u0026gt;\u0026gt;John: Fight against hypochondria end Note right of John: Rational thoughts! John--\u0026gt;\u0026gt;Alice: Great! John-\u0026gt;\u0026gt;Bob: How about you? Bob--\u0026gt;\u0026gt;John: Jolly good! An example Gantt diagram:\n```mermaid gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d ``` renders …","date":1562889600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1562889600,"objectID":"07e02bccc368a192a0c76c44918396c3","permalink":"https://ckarag.github.io/post/writing-technical-content/","publishdate":"2019-07-12T00:00:00Z","relpermalink":"/post/writing-technical-content/","section":"post","summary":"Wowchemy is designed to give technical content creators a seamless experience. You can focus on the content and Wowchemy handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.","tags":null,"title":"Writing technical content in Markdown","type":"post"},{"authors":null,"categories":null,"content":"In this project I employed neural networks to explore whether I can increase the realized portfolio return obtained from the long-component of the academically motivated low-beta factor strategy in the US equities space. The network was set to forecast the next-day trade direction signal of several pre-selected stocks –the pool of low-beta stocks. The idea of employing an ANN signal generation model, was to add a second layer of improvement on the already over-performing stocks as implied by the market anomaly and the underlying factor strategy. The network successfully detects buy signals allowing to “hand-pick” several stocks from an already well-performing pool of stocks. My ANN-based investment strategy systematically achieves portfolio gains (as captured by increases in risk-adjusted returns) over the benchmarks. In particularly, over the period 2009-2017, my algorithmic strategy on average increases the Sharpe ratios by 65% and triples the FF5-alphas compared to the standard overperforming low-beta factor decile portfolios (P1-P3). My best performing ANN portfolio (comprised of the 330 lowest-beta US stocks, before “hand-picked” by the network) achieves a 126% increase, resulting a Sharpe ratio of 5, translating to a ratio of 7.7 relative to the Sharpe of the S\u0026amp;P500 with dividends reinvested.\n","date":1562716800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1562716800,"objectID":"fa3e0b19fa7f606b774b3494175d7c99","permalink":"https://ckarag.github.io/project/ann_bab/","publishdate":"2019-07-10T00:00:00Z","relpermalink":"/project/ann_bab/","section":"project","summary":"Employing an ANN trade-direction signal generator to boost the realized returns obtained from the decile portfolios of the beta anomaly in the US equities market.","tags":["Machine Learning","Alpha"],"title":"A Trade-signal Generator for Improving Portfolio Returns of Factor Strategies","type":"project"},{"authors":["admin"],"categories":null,"content":" Create your slides in Markdown - click the Slides button to check out the example. Supplementary notes can be added here, including code, math, and images.\n","date":1554595200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1554595200,"objectID":"557dc08fd4b672a0c08e0a8cf0c9ff7d","permalink":"https://ckarag.github.io/publication/preprint/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/preprint/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example preprint / working paper","type":"publication"},{"authors":["admin"],"categories":[],"content":"from IPython.core.display import Image Image(\u0026#39;https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png\u0026#39;) print(\u0026#34;Welcome to Academic!\u0026#34;) Welcome to Academic! Install Python and JupyterLab Install Anaconda which includes Python 3 and JupyterLab.\nAlternatively, install JupyterLab with pip3 install jupyterlab.\nCreate or upload a Jupyter notebook Run the following commands in your Terminal, substituting \u0026lt;MY-WEBSITE-FOLDER\u0026gt; and \u0026lt;SHORT-POST-TITLE\u0026gt; with the file path to your Academic website folder and a short title for your blog post (use hyphens instead of spaces), respectively:\nmkdir -p \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/ cd \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/ jupyter lab index.ipynb The jupyter command above will launch the JupyterLab editor, allowing us to add Academic metadata and write the content.\nEdit your post metadata The first cell of your Jupter notebook will contain your post metadata (front matter).\nIn Jupter, choose Markdown as the type of the first cell and wrap your Academic metadata in three dashes, indicating that it is YAML front matter:\n--- title: My post\u0026#39;s title date: 2019-09-01 # Put any other Academic metadata here... --- Edit the metadata of your post, using the documentation as a guide to the available options.\nTo set a featured image, place an image named featured into your post’s folder.\nFor other tips, such as using math, see the guide on writing content with Academic.\nConvert notebook to Markdown jupyter nbconvert index.ipynb --to markdown --NbConvertApp.output_files_dir=. Example This post was created with Jupyter. The orginal files can be found at https://github.com/gcushen/hugo-academic/tree/master/exampleSite/content/post/jupyter\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567641600,"objectID":"6e929dc84ed3ef80467b02e64cd2ed64","permalink":"https://ckarag.github.io/post/jupyter/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/post/jupyter/","section":"post","summary":"Learn how to blog in Academic using Jupyter notebooks","tags":[],"title":"Display Jupyter Notebooks with Academic","type":"post"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Wowchemy Wowchemy | Documentation\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026#34;blueberry\u0026#34; if porridge == \u0026#34;blueberry\u0026#34;: print(\u0026#34;Eating...\u0026#34;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne Two Three A fragment can accept two optional parameters:\nclass: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\nOnly the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026#34;/media/boards.jpg\u0026#34; \u0026gt;}} {{\u0026lt; slide background-color=\u0026#34;#0000FF\u0026#34; \u0026gt;}} {{\u0026lt; slide class=\u0026#34;my-style\u0026#34; \u0026gt;}} Custom CSS Example Let’s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://ckarag.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Wowchemy's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":null,"categories":null,"content":"","date":1544313600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1544313600,"objectID":"60552c57f4035741554d5cfde698b784","permalink":"https://ckarag.github.io/project/productivitycy/","publishdate":"2018-12-09T00:00:00Z","relpermalink":"/project/productivitycy/","section":"project","summary":"We estimate labour and total-factor (TFP) productivity for Cyprus, using the growth accounting framework. We then present developments and provide public policy recommendations with the objective of increasing productivity and consequently GDP growth in Cyprus. Visit [CypERC](https://ucyweb.ucy.ac.cy/erc/en/ \"UCY\") for updates.","tags":["Policy"],"title":"Productivity in Cyprus","type":"project"},{"authors":["admin","Robert Ford"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Create your slides in Markdown - click the Slides button to check out the example. Supplementary notes can be added here, including code, math, and images.\n","date":1441065600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1441065600,"objectID":"966884cc0d8ac9e31fab966c4534e973","permalink":"https://ckarag.github.io/publication/journal-article/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/journal-article/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example journal article","type":"publication"},{"authors":["admin","Robert Ford"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Create your slides in Markdown - click the Slides button to check out the example. Supplementary notes can be added here, including code, math, and images.\n","date":1372636800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1372636800,"objectID":"69425fb10d4db090cfbd46854715582c","permalink":"https://ckarag.github.io/publication/conference-paper/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/conference-paper/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":[],"title":"An example conference paper","type":"publication"},{"authors":null,"categories":null,"content":"2024 [MATLAB] DFGLS for MATLAB\nDF-GLS unit root test of Elliott, Rothenberg \u0026amp; Stock (1996), with 3 optimal lag-length selection methods (SIC, MAIC, Sequential-t) for selecting the lagged terms in the underlying ADF regression.\nDownload - tutorial\n[MATLAB] The Real-Time Nowcasting Suite\nAn extensive collection of uniformly designed MATLAB functions developed for pre-processing large sets of mixed-frequency data with missing observations at the bottom (ragged-edges) and tailored for forecasting economic indicators in real-time.\nDescription - Download (available shortly) - tutorial (under development)\n2022 [MATLAB] Interface to easily access FRED® data\nDownload - tutorial\n[MATLAB] Collaborator to the MIDAS-ML (MIDAS Sparse-Group LASSO) Package by Jonas Striaukas\nDownload\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"9260a1bd38ad457fcbbc5d4ca2b02951","permalink":"https://ckarag.github.io/codes/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/codes/","section":"","summary":"2024 [MATLAB] DFGLS for MATLAB\nDF-GLS unit root test of Elliott, Rothenberg \u0026 Stock (1996), with 3 optimal lag-length selection methods (SIC, MAIC, Sequential-t) for selecting the lagged terms in the underlying ADF regression.","tags":null,"title":"Codes","type":"page"},{"authors":null,"categories":null,"content":"The $ \\alpha $4casting real-time M$ \\alpha $cro Monitor uses state-of-the-art datasets and statistical models developed exclusively for macroeconomic nowcasting, allowing to closely and accurately monitor economic activity in real-time. The predictive and pre-processing models used to produce our nowcasts and forecasts, follow research that has been published in top academic journals (JBES, Econometrica to name a few) following the scientific developments in predictive macro modelling of the last couple of years. The models are evaluated frequently against their competing alternatives to guarantee leading preformance among the industry. The Macro Monitor avoids human judgment by operating entirely on a large pool of data released by official sources. Data are retreived from the U.S. Census Bureau, U.S. Bureau of Labor Statistics, and other sources. It is fully automated and the predictions are continuously updated in real-time as soon as new data releases become available, allowing to incorporate the latest market and economic developments, hence providing the most up-to-date views for the current and future states of the economies being monitored.\nUS The graphs below show the weekly evolution of the nowcasts for the respective period, as models are updated each Friday to reflect newly released information from the preceding week. The realized figures (labelled as ‘Actual’) reflect the revisions from the official source made at the 2 most recent releases (as indicated by the ‘as of’ date in the hover-over text). You can access the spreadsheets containing the latest nowcasts and forecasts as well as the full history of the projections here.\nForecast Dates 2024Q2 2024Q3 2024Q4 28/06/2024 4.33 2.42 2.65 05/07/2024 3.4 4.46 2.64 12/07/2024 3.22 4.78 2.64 19/07/2024 4.1 4.49 2.68 US Real GDP growth Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 3.25 3.17 3.24 05/07/2024 3.46 3.2 3.24 12/07/2024 2.93 2.76 19/07/2024 2.93 2.77 US Headline CPI Inflation Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 2.61 3.18 3.56 05/07/2024 3.75 2.59 3.49 12/07/2024 2.06 2.9 19/07/2024 1.92 2.79 US Core CPI Inflation Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 -0.01 -0.02 0.04 05/07/2024 -0.01 -0.02 12/07/2024 -0.01 -0.02 19/07/2024 -0.01 -0.01 US Unemployment Rate Change Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 0.12 0.11 0.04 05/07/2024 0.13 0.12 12/07/2024 0.12 0.11 19/07/2024 0.1 0.09 US Payroll employment (nonfarm, SA) Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 5.47 2.44 2.31 05/07/2024 13.04 10.36 1.85 12/07/2024 13.99 8.97 4.02 19/07/2024 3.86 3.79 US Industrial Production Visualizing the determinants of nowcasts and how those have changed across time. Disclaimer: The views expressed here are my own and do not reflect those of any institutions I am affiliated with. ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"e847d22979794a12a5c43a2eaaf663ef","permalink":"https://ckarag.github.io/rtmonitor/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/rtmonitor/","section":"","summary":"The $ \\alpha $4casting real-time M$ \\alpha $cro Monitor uses state-of-the-art datasets and statistical models developed exclusively for macroeconomic nowcasting, allowing to closely and accurately monitor economic activity in real-time. The predictive and pre-processing models used to produce our nowcasts and forecasts, follow research that has been published in top academic journals (JBES, Econometrica to name a few) following the scientific developments in predictive macro modelling of the last couple of years.","tags":null,"title":"M$ \\alpha $cro Monitor","type":"page"},{"authors":null,"categories":null,"content":"Nowcasting with State-of-the-Art Methodologies The purpose of the nowcasts in this section is not to give a single number, rather it is to give an indication of how well the latest research published in top academic journals, and highly cited methodologies for nowcasting, perform on a real-time basis.\nThe 1st two plots contain nowcasts based on two state-of-the-art nowcasting methodologies. The two methodologies are the Factor-augmented AR (FAR) methodology of Stock-Watson (2002), and the Sg-LASSO-MIDAS by Babii et al. (2022). The nowcasts at the last plot are based on the tutorial material I am teaching for the MSc course titled ‘Intro to Big Data Analytics’ at KCL. In the two plots at the top, all the simplifications made in the course are dropped.\nThe dataset is made of 160 carefully selected mixed-frequency indicators, that are updated on a timely basis (i.e. every time the nowcasts are re-run). As such, the nowcasts reflect the information contained in the latest released economic and market data, as of the day of the estimation (which can be seen by hovering over the corresponding points in the plots). The mixed-frequency panel of predictors contains weekly, daily, and monthly indicators. The series that is nowcasted is the annualized MoM% headline CPI for the US (FRED mnemonic: CPIAUCSL).\nSOTA Nowcasts\nMSc Nowcasts\nReferences Babii, A., Ghysels E., \u0026amp; Striaukas, J. (2022). “Machine learning time series regressions with an application to nowcasting.” Journal of Business \u0026amp; Economic Statistics, 40(3), 1094-1106. Stock, J. H., \u0026amp; Watson, M. W. (2002). “Macroeconomic forecasting using diffusion indexes.” Journal of Business \u0026amp; Economic Statistics, 20(2), 147-162. ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"d2f3737e447a3be56cf02f948e029668","permalink":"https://ckarag.github.io/sota/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/sota/","section":"","summary":"Nowcasting with State-of-the-Art Methodologies The purpose of the nowcasts in this section is not to give a single number, rather it is to give an indication of how well the latest research published in top academic journals, and highly cited methodologies for nowcasting, perform on a real-time basis.","tags":null,"title":"Macro Nowcasting","type":"page"},{"authors":null,"categories":null,"content":"List of forecasting models Acronym Model Description AR(P) Autoregressive iterated-specification RW Random walk ARDI(K,BIC) Autoregressive diffusion indices with K factors. Optimal lag-order via BIC T.ARDI(K,BIC) ARDI with target-factors. Hard-threshold set to |t-stat|\u0026gt;1.96 BVAR-Minn Homoscedastic large Bayesian VAR BVAR-CSV Large Bayesian VAR with heteroscedastic innovations BBoost Quadratic-loss L2 boosting, block-wise CBoost Quadratic-loss L2 boosting, component-wise CSR Complete Subset Regressions (20C4) with hard-thresholding preselection Bag Linear bagging with hard-thresholding preselection BTree Boosting regression trees RF Random forests SVR Support vector machine regression with Gaussian Kernel function Ridge Ridge regression LASSO Least absolute shrinkage and selection operator with BIC for lambda AdaLASSO Adaptive LASSO EN Elastic Net AdaEN Adaptive EN LSTM Long-short-term memory RNN with 3-hidden layers SgLASSO Sparse-group-LASSO-MIDAS with block-K-fold CV for lambda \u0026amp; alpha List of methods for treating mixed-frequencies Acronym Transformation Description D1 Down-sampling to Quarterly (Temporal aggregation with equal-weights) D2 Unrestricted MIDAS Polynomials D3 Legendre Polynomials (3rd degree) The Ranking: Real-time model evaluation Models n=0 n=1 n=2 n=3 n=4 avg Bag-D3 0.728 0.932 0.972 0.998 0.994 0.925 CSR-D2 0.707 0.938 0.976 1 1.004 0.925 BBoost-D1F 0.725 0.925 0.978 1.006 1.001 0.927 Bag-D2F 0.772 0.91 0.967 0.994 0.999 0.929 Ridge-D1 0.764 0.924 0.958 0.996 1.001 0.929 Ridge-D2 0.742 0.926 0.972 1.001 1.005 0.929 Bag-D2 0.767 0.921 0.969 0.993 1.002 0.93 T.ARDI(2) 0.745 0.929 0.975 1.002 1.007 0.932 Bag-D1F 0.759 0.925 0.97 1.005 1.001 0.932 Bag-D1 0.783 0.918 0.961 0.996 1.002 0.932 CSR-D1 0.758 0.934 0.974 1.004 1.009 0.936 Ridge-D3 0.791 0.921 0.969 1.004 1.003 0.938 RF-D1F 0.816 0.917 0.966 0.995 0.992 0.938 Ridge-D2F 0.808 0.913 0.968 0.999 1.004 0.938 CBoost-D1F 0.773 0.925 0.983 1.01 1.001 0.938 SVR-D1 0.825 0.919 0.964 0.992 0.995 0.939 LASSO-D1F 0.787 0.93 0.978 1.002 0.999 0.939 AdaLASSO-D1F 0.781 0.937 0.98 1.003 0.998 0.94 RF-D1 0.804 0.924 0.971 0.999 1.003 0.94 Bag-D3F 0.816 0.917 0.972 1.005 0.993 0.941 EN-D2F 0.816 0.921 0.969 0.999 1.001 0.941 EN-D1F 0.799 0.928 0.978 1.001 1 0.941 SVR-D3 0.827 0.92 0.97 0.995 0.997 0.942 RF-D3 0.812 0.936 0.969 0.994 1.001 0.942 ARDI(1) 0.799 0.939 0.975 0.999 1.001 0.943 CBoost-D2F 0.821 0.92 0.965 0.999 1.008 0.943 BBoost-D2F 0.836 0.912 0.967 0.998 1 0.943 SVR-D3F 0.831 0.914 0.972 0.995 1.001 0.943 SVR-D2 0.828 0.923 0.971 0.997 0.997 0.943 AdaEN-D2 0.766 0.947 0.977 1.003 1.024 0.943 EN-D2 0.777 0.971 0.978 0.996 0.994 0.943 RF-D2 0.816 0.925 0.976 1.001 1 0.943 LASSO-D2F 0.831 0.92 0.969 0.998 1 0.944 AdaEN-D1F 0.802 0.934 0.981 1.002 1 0.944 SVR-D1F 0.827 0.921 0.971 0.999 1 0.944 CSR-D2F 0.824 0.928 0.97 0.997 1 0.944 SVR-D2F 0.831 0.925 0.972 0.996 0.995 0.944 RF-D2F 0.827 0.929 0.972 0.998 0.995 0.944 BTree-D1F 0.811 0.923 0.975 1.002 1.008 0.944 AdaEN-D2F 0.834 0.926 0.965 0.999 1.002 0.945 AdaLASSO-D2F 0.84 0.922 0.966 0.998 1.001 0.945 AdaLASSO-D2 0.765 0.954 0.979 0.998 1.034 0.946 LSTM-D1F 0.845 0.918 0.968 1.005 0.994 0.946 ARDI(2) 0.798 0.947 0.978 1.001 1.008 0.946 EN-D3 0.834 0.924 0.975 1 1 0.947 CSR-D3 0.814 0.931 0.98 1.005 1.003 0.947 RW 0.839 0.921 0.975 0.999 0.999 0.947 LSTM-D2F 0.844 0.928 0.966 1 0.997 0.947 LASSO-D2 0.78 0.98 0.983 1 0.997 0.948 BVAR-CSV 0.811 0.926 0.98 1.012 1.013 0.948 RF-D3F 0.844 0.934 0.976 0.991 1.001 0.949 T.ARDI(1) 0.814 0.948 0.975 1.005 1.007 0.95 AdaEN-D1 0.817 0.931 0.991 1.007 1.003 0.95 BBoost-D3F 0.865 0.906 0.973 1.006 1.001 0.95 AdaEN-D3 0.848 0.928 0.976 1 1.002 0.951 LSTM-D3F 0.851 0.926 0.972 0.994 1.013 0.951 SgLASSO-D3 0.849 0.925 0.978 1.003 1.005 0.952 SgLASSO-D3F 0.861 0.912 0.977 1.011 1.003 0.953 CSR-D1F 0.877 0.925 0.97 0.998 0.999 0.954 EN-D1 0.809 0.936 0.944 1.045 1.036 0.954 BTree-D1 0.812 0.933 0.988 1.015 1.024 0.955 AdaEN-D3F 0.883 0.924 0.968 1.001 0.999 0.955 LSTM-D3 0.858 0.932 0.978 1.005 1.004 0.955 BVAR-Minn 0.85 0.933 0.974 1.012 1.015 0.957 EN-D3F 0.891 0.926 0.971 1 0.999 0.957 CBoost-D3F 0.854 0.936 0.985 1.013 1.001 0.958 LSTM-D1 0.866 0.936 0.978 1.002 1.013 0.959 CSR-D3F 0.89 0.938 0.97 1.001 0.997 0.959 LASSO-D3F 0.903 0.921 0.971 1 1.003 0.96 AdaLASSO-D3F 0.893 0.929 0.971 1.003 1.004 0.96 BTree-D2F 0.85 0.954 0.991 1.017 0.997 0.962 LSTM-D2 0.868 0.943 0.986 1.017 0.999 0.962 BTree-D2 0.839 0.952 0.994 1.012 1.016 0.963 Ridge-D3F 0.802 0.941 1.017 1.046 1.033 0.968 AdaLASSO-D1 0.83 0.947 1.019 1.037 1.022 0.971 BTree-D3 0.851 0.988 0.99 1.016 1.021 0.973 Ridge-D1F 0.779 0.959 1.026 1.06 1.054 0.976 LASSO-D3 0.832 0.932 1.018 1.06 1.056 0.98 AdaLASSO-D3 0.834 0.945 1.03 1.065 1.06 0.987 LASSO-D1 0.774 1.007 0.987 1.102 1.074 0.989 BTree-D3F 0.88 1.031 0.999 1.01 1.033 0.991 AR(BIC) 1.008 1.003 1 1.001 1 1.002 AR(CV) 1.02 1.004 1 1.001 0.999 1.005 AR(4) 1.049 1.017 1.002 0.999 0.999 1.013 AR(1) NaN NaN NaN NaN NaN NaN ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"6d267b6bb6f97459260d2aef4d0f4b62","permalink":"https://ckarag.github.io/bmnr/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/bmnr/","section":"","summary":"List of forecasting models Acronym Model Description AR(P) Autoregressive iterated-specification RW Random walk ARDI(K,BIC) Autoregressive diffusion indices with K factors. Optimal lag-order via BIC T.ARDI(K,BIC) ARDI with target-factors. Hard-threshold set to |t-stat|\u003e1.","tags":null,"title":"The Big Macro Nowcasting Ranking","type":"page"}] \ No newline at end of file +[{"authors":null,"categories":null,"content":"Haris Karagiannakis is a PhD candidate currently working on developing Machine Learning methodologies to improve real-time monitoring of economic activity. Haris has studied both economics and finance at a postgraduate level. He holds an M.Sc. in Economics from the London School of Economics and Political Science and an M.Sc. in Financial Economics from the University of Cyprus, while he also obtained merit-based scholarships to fund both his undergraduate and postgraduate studies. He has also earned various awards and distinctions in regional and international competitions for his undergraduate and postgraduate theses. Prior joining KCL, he worked as a research officer (between 2010 and 2019), involved in applied cutting-edge quantitative research on account of organizations and companies. His research interests include macro-economic forecasting and factor investment strategies. His research is fully funded by the Qatar Centre for Global Banking \u0026amp; Finance (QCGBF) at KCL. Haris has also been teaching various MSc courses at KCL, including Intro to Big Data Analytics and Stats Software for Finance.\nDownload my resumé .\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"e577d600689cd6980485bd14a2fd1b76","permalink":"","publishdate":"0001-01-01T00:00:00Z","relpermalink":"","section":"authors","summary":"Haris Karagiannakis is a PhD candidate currently working on developing Machine Learning methodologies to improve real-time monitoring of economic activity. Haris has studied both economics and finance at a postgraduate level.","tags":null,"title":"Haris Karagiannakis","type":"authors"},{"authors":null,"categories":null,"content":"Generated by Wowchemy - the FREE, Hugo-based open source website builder trusted by 500,000+ sites.\nEasily build anything with blocks - no-code required!\nFrom landing pages, second brains, and courses to academic resumés, conferences, and tech blogs.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"92097bd738c772f382c3e53d60846459","permalink":"https://ckarag.github.io/home-unused/demo-hero/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/demo-hero/","section":"home-unused","summary":"Generated by Wowchemy - the FREE, Hugo-based open source website builder trusted by 500,000+ sites.\nEasily build anything with blocks - no-code required!\nFrom landing pages, second brains, and courses to academic resumés, conferences, and tech blogs.","tags":null,"title":"Hugo Academic Theme","type":"home-unused"},{"authors":null,"categories":null,"content":"👋 Welcome to the Academic Template The Wowchemy Academic Resumé Template for Hugo empowers you to create your job-winning online resumé and showcase your academic publications.\nCheck out the latest demo of what you’ll get in less than 10 minutes, or view the showcase.\nWowchemy makes it easy to create a beautiful website for free. Edit your site in Markdown, Jupyter, or RStudio (via Blogdown), generate it with Hugo, and deploy with GitHub or Netlify. Customize anything on your site with widgets, themes, and language packs.\n👉 Get Started 📚 View the documentation 💬 Chat with the Wowchemy community or Hugo community 🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy 💡 Request a feature or report a bug for Wowchemy ⬆️ Updating Wowchemy? View the Update Guide and Release Notes Crowd-funded open-source software To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.\n❤️ Click here to unlock rewards with sponsorship You’re looking at a Wowchemy widget This homepage section is an example of adding elements to the Blank widget.\nBackgrounds can be applied to any section. Here, the background option is set give a color gradient.\nTo remove this section, delete content/home/demo.md.\nGet inspired Check out the Markdown files which power the Academic Demo, or view the showcase.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"1905ee7535e0a4535c48727a4bc5d258","permalink":"https://ckarag.github.io/home-unused/demo-links/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/demo-links/","section":"home-unused","summary":"👋 Welcome to the Academic Template The Wowchemy Academic Resumé Template for Hugo empowers you to create your job-winning online resumé and showcase your academic publications.\nCheck out the latest demo of what you’ll get in less than 10 minutes, or view the showcase.","tags":null,"title":"Academic Template","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"d6682c06ff2f3dd0fc28f7e2c0702d07","permalink":"https://ckarag.github.io/home-unused/experience/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/experience/","section":"home-unused","summary":"","tags":null,"title":"Experience","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"9e909a8894fd21a2eff4b3e43238d81e","permalink":"https://ckarag.github.io/home-unused/accomplishments/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/accomplishments/","section":"home-unused","summary":"","tags":null,"title":"Accomplish\u0026shy;ments","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"0e643989bdefe366f2b5fddf949a36b6","permalink":"https://ckarag.github.io/home-unused/posts/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/posts/","section":"home-unused","summary":"","tags":null,"title":"Recent Posts","type":"home-unused"},{"authors":null,"categories":null,"content":" ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"6d5b97766bbd075b2dc107b374efc3bc","permalink":"https://ckarag.github.io/home-unused/gallery/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/gallery/","section":"home-unused","summary":" ","tags":null,"title":"Gallery","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"d927b251d3da15a737d1f66fb88d4504","permalink":"https://ckarag.github.io/home-unused/talks/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/talks/","section":"home-unused","summary":"","tags":null,"title":"Recent \u0026 Upcoming Talks","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"28f54f6e819207239a6024bbaa9d78de","permalink":"https://ckarag.github.io/home-unused/featured/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/featured/","section":"home-unused","summary":"","tags":null,"title":"Featured Publications","type":"home-unused"},{"authors":null,"categories":null,"content":" Quickly discover relevant content by filtering publications. ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"19cfbeefa99b41865496b68f2fb35bad","permalink":"https://ckarag.github.io/home-unused/publications/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/publications/","section":"home-unused","summary":" Quickly discover relevant content by filtering publications. ","tags":null,"title":"Recent Publications","type":"home-unused"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"657179738bed56748434d6ae76e8a647","permalink":"https://ckarag.github.io/home-unused/tags/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home-unused/tags/","section":"home-unused","summary":"","tags":null,"title":"Popular Topics","type":"home-unused"},{"authors":[],"categories":null,"content":" Click on the Slides button above to view the built-in slides feature. Slides can be added in a few ways:\nCreate slides using Wowchemy’s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes. Further event details, including page elements such as image galleries, can be added to the body of this page.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"a8edef490afe42206247b6ac05657af0","permalink":"https://ckarag.github.io/talk/example-talk/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/example-talk/","section":"event","summary":"An example talk using Wowchemy's Markdown slides feature.","tags":[],"title":"Example Talk","type":"event"},{"authors":["admin","吳恩達"],"categories":["Demo","教程"],"content":"import libr print(\u0026#39;hello\u0026#39;) Overview The Wowchemy website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more Get Started 👉 Create a new site 📚 Personalize your site 💬 Chat with the Wowchemy community or Hugo community 🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy 💡 Request a feature or report a bug for Wowchemy ⬆️ Updating Wowchemy? View the Update Tutorial and Release Notes Crowd-funded open-source software To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.\n❤️ Click here to become a sponsor and help support Wowchemy’s future ❤️ As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨\nEcosystem Hugo Academic CLI: Automatically import publications from BibTeX Inspiration Check out the latest demo of what you’ll get in less than 10 minutes, or view the showcase of personal, project, and business sites.\nFeatures Page builder - Create anything with widgets and elements Edit any type of content - Blog posts, publications, talks, slides, projects, and more! Create content in Markdown, Jupyter, or RStudio Plugin System - Fully customizable color and font themes Display Code and Math - Code highlighting and LaTeX math supported Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more! Beautiful Site - Simple and refreshing one page design Industry-Leading SEO - Help get your website found on search engines and social media Media Galleries - Display your images and videos with captions in a customizable gallery Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site Multi-language - 34+ language packs including English, 中文, and Português Multi-user - Each author gets their own profile page Privacy Pack - Assists with GDPR Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects One-Click Deployment - No servers. No databases. Only files. Themes Wowchemy and its templates come with automatic day (light) and night (dark) mode built-in. Alternatively, visitors can choose their preferred mode - click the moon icon in the top right of the Demo to see it in action! Day/night mode can also be disabled by the site admin in params.toml.\nChoose a stunning theme and font for your site. Themes are fully customizable.\nLicense Copyright 2016-present George Cushen.\nReleased under the MIT license.\n","date":1607817600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1607817600,"objectID":"279b9966ca9cf3121ce924dca452bb1c","permalink":"https://ckarag.github.io/post/getting-started/","publishdate":"2020-12-13T00:00:00Z","relpermalink":"/post/getting-started/","section":"post","summary":"Welcome 👋 We know that first impressions are important, so we've populated your new site with some initial content to help you get familiar with everything in no time.","tags":["Academic","开源"],"title":"Welcome to Wowchemy, the website builder for Hugo","type":"post"},{"authors":null,"categories":null,"content":"Visualizing the change in inflation dynamics as captured by LASSO using a large macro dataset (FRED-MD). Click here to see an animated (gif) version of the word clouds.\n","date":1583971200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1583971200,"objectID":"4a3d7ff33938b41d752e0123a79cafa7","permalink":"https://ckarag.github.io/project/inflationdyn/","publishdate":"2020-03-12T00:00:00Z","relpermalink":"/project/inflationdyn/","section":"project","summary":"Visualizing the change in inflation dynamics as captured by LASSO using a large macro dataset (FRED-MD). See {{\u003c staticref \"MLinterpret_CPI.html\" \"newtab\" \u003e}}here{{\u003c /staticref \u003e}} for an animated (gif) version.","tags":["Machine Learning","Forecasting"],"title":"US Inflation Dynamics","type":"project"},{"authors":null,"categories":null,"content":"Wowchemy is designed to give technical content creators a seamless experience. You can focus on the content and Wowchemy handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.\nOn this page, you’ll find some examples of the types of technical content that can be rendered with Wowchemy.\nExamples Code Wowchemy supports a Markdown extension for highlighting code syntax. You can customize the styles under the syntax_highlighter option in your config/_default/params.yaml file.\n```python import pandas as pd data = pd.read_csv(\u0026#34;data.csv\u0026#34;) data.head() ``` renders as\nimport pandas as pd data = pd.read_csv(\u0026#34;data.csv\u0026#34;) data.head() Mindmaps Wowchemy supports a Markdown extension for mindmaps.\nSimply insert a Markdown markmap code block and optionally set the height of the mindmap as shown in the example below.\nA simple mindmap defined as a Markdown list:\n```markmap {height=\u0026#34;200px\u0026#34;} - Hugo Modules - wowchemy - wowchemy-plugins-netlify - wowchemy-plugins-netlify-cms - wowchemy-plugins-reveal ``` renders as\n- Hugo Modules - wowchemy - wowchemy-plugins-netlify - wowchemy-plugins-netlify-cms - wowchemy-plugins-reveal A more advanced mindmap with formatting, code blocks, and math:\n```markmap - Mindmaps - Links - [Wowchemy Docs](https://wowchemy.com/docs/) - [Discord Community](https://discord.gg/z8wNYzb) - [GitHub](https://github.com/wowchemy/wowchemy-hugo-themes) - Features - Markdown formatting - **inline** ~~text~~ *styles* - multiline text - `inline code` - ```js console.log(\u0026#39;hello\u0026#39;); console.log(\u0026#39;code block\u0026#39;); ``` - Math: $x = {-b \\pm \\sqrt{b^2-4ac} \\over 2a}$ ``` renders as\n- Mindmaps - Links - [Wowchemy Docs](https://wowchemy.com/docs/) - [Discord Community](https://discord.gg/z8wNYzb) - [GitHub](https://github.com/wowchemy/wowchemy-hugo-themes) - Features - Markdown formatting - **inline** ~~text~~ *styles* - multiline text - `inline code` - ```js console.log(\u0026#39;hello\u0026#39;); console.log(\u0026#39;code block\u0026#39;); ``` - Math: $x = {-b \\pm \\sqrt{b^2-4ac} \\over 2a}$ Charts Wowchemy supports the popular Plotly format for interactive charts.\nSave your Plotly JSON in your page folder, for example line-chart.json, and then add the {{\u0026lt; chart data=\u0026#34;line-chart\u0026#34; \u0026gt;}} shortcode where you would like the chart to appear.\nDemo:\nYou might also find the Plotly JSON Editor useful.\nMath Wowchemy supports a Markdown extension for $\\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.\nTo render inline or block math, wrap your LaTeX math with {{\u0026lt; math \u0026gt;}}$...${{\u0026lt; /math \u0026gt;}} or {{\u0026lt; math \u0026gt;}}$$...$${{\u0026lt; /math \u0026gt;}}, respectively. (We wrap the LaTeX math in the Wowchemy math shortcode to prevent Hugo rendering our math as Markdown. The math shortcode is new in v5.5-dev.)\nExample math block:\n{{\u0026lt; math \u0026gt;}} $$ \\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |}{\\left \\|\\nabla F(\\mathbf{x}_{n}) - \\nabla F(\\mathbf{x}_{n-1}) \\right \\|^2} $$ {{\u0026lt; /math \u0026gt;}} renders as\n$$\\gamma_{n} = \\frac{ \\left | \\left (\\mathbf x_{n} - \\mathbf x_{n-1} \\right )^T \\left [\\nabla F (\\mathbf x_{n}) - \\nabla F (\\mathbf x_{n-1}) \\right ] \\right |}{\\left \\|\\nabla F(\\mathbf{x}_{n}) - \\nabla F(\\mathbf{x}_{n-1}) \\right \\|^2}$$ Example inline math {{\u0026lt; math \u0026gt;}}$\\nabla F(\\mathbf{x}_{n})${{\u0026lt; /math \u0026gt;}} renders as $\\nabla F(\\mathbf{x}_{n})$.\nExample multi-line math using the math linebreak (\\\\):\n{{\u0026lt; math \u0026gt;}} $$f(k;p_{0}^{*}) = \\begin{cases}p_{0}^{*} \u0026amp; \\text{if }k=1, \\\\ 1-p_{0}^{*} \u0026amp; \\text{if }k=0.\\end{cases}$$ {{\u0026lt; /math \u0026gt;}} renders as\n$$ f(k;p_{0}^{*}) = \\begin{cases}p_{0}^{*} \u0026amp; \\text{if }k=1, \\\\ 1-p_{0}^{*} \u0026amp; \\text{if }k=0.\\end{cases} $$ Diagrams Wowchemy supports a Markdown extension for diagrams. You can enable this feature by toggling the diagram option in your config/_default/params.toml file or by adding diagram: true to your page front matter.\nAn example flowchart:\n```mermaid graph TD A[Hard] --\u0026gt;|Text| B(Round) B --\u0026gt; C{Decision} C --\u0026gt;|One| D[Result 1] C --\u0026gt;|Two| E[Result 2] ``` renders as\ngraph TD A[Hard] --\u0026gt;|Text| B(Round) B --\u0026gt; C{Decision} C --\u0026gt;|One| D[Result 1] C --\u0026gt;|Two| E[Result 2] An example sequence diagram:\n```mermaid sequenceDiagram Alice-\u0026gt;\u0026gt;John: Hello John, how are you? loop Healthcheck John-\u0026gt;\u0026gt;John: Fight against hypochondria end Note right of John: Rational thoughts! John--\u0026gt;\u0026gt;Alice: Great! John-\u0026gt;\u0026gt;Bob: How about you? Bob--\u0026gt;\u0026gt;John: Jolly good! ``` renders as\nsequenceDiagram Alice-\u0026gt;\u0026gt;John: Hello John, how are you? loop Healthcheck John-\u0026gt;\u0026gt;John: Fight against hypochondria end Note right of John: Rational thoughts! John--\u0026gt;\u0026gt;Alice: Great! John-\u0026gt;\u0026gt;Bob: How about you? Bob--\u0026gt;\u0026gt;John: Jolly good! An example Gantt diagram:\n```mermaid gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d ``` renders …","date":1562889600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1562889600,"objectID":"07e02bccc368a192a0c76c44918396c3","permalink":"https://ckarag.github.io/post/writing-technical-content/","publishdate":"2019-07-12T00:00:00Z","relpermalink":"/post/writing-technical-content/","section":"post","summary":"Wowchemy is designed to give technical content creators a seamless experience. You can focus on the content and Wowchemy handles the rest.\nHighlight your code snippets, take notes on math classes, and draw diagrams from textual representation.","tags":null,"title":"Writing technical content in Markdown","type":"post"},{"authors":null,"categories":null,"content":"In this project I employed neural networks to explore whether I can increase the realized portfolio return obtained from the long-component of the academically motivated low-beta factor strategy in the US equities space. The network was set to forecast the next-day trade direction signal of several pre-selected stocks –the pool of low-beta stocks. The idea of employing an ANN signal generation model, was to add a second layer of improvement on the already over-performing stocks as implied by the market anomaly and the underlying factor strategy. The network successfully detects buy signals allowing to “hand-pick” several stocks from an already well-performing pool of stocks. My ANN-based investment strategy systematically achieves portfolio gains (as captured by increases in risk-adjusted returns) over the benchmarks. In particularly, over the period 2009-2017, my algorithmic strategy on average increases the Sharpe ratios by 65% and triples the FF5-alphas compared to the standard overperforming low-beta factor decile portfolios (P1-P3). My best performing ANN portfolio (comprised of the 330 lowest-beta US stocks, before “hand-picked” by the network) achieves a 126% increase, resulting a Sharpe ratio of 5, translating to a ratio of 7.7 relative to the Sharpe of the S\u0026amp;P500 with dividends reinvested.\n","date":1562716800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1562716800,"objectID":"fa3e0b19fa7f606b774b3494175d7c99","permalink":"https://ckarag.github.io/project/ann_bab/","publishdate":"2019-07-10T00:00:00Z","relpermalink":"/project/ann_bab/","section":"project","summary":"Employing an ANN trade-direction signal generator to boost the realized returns obtained from the decile portfolios of the beta anomaly in the US equities market.","tags":["Machine Learning","Alpha"],"title":"A Trade-signal Generator for Improving Portfolio Returns of Factor Strategies","type":"project"},{"authors":["admin"],"categories":null,"content":" Create your slides in Markdown - click the Slides button to check out the example. Supplementary notes can be added here, including code, math, and images.\n","date":1554595200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1554595200,"objectID":"557dc08fd4b672a0c08e0a8cf0c9ff7d","permalink":"https://ckarag.github.io/publication/preprint/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/preprint/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example preprint / working paper","type":"publication"},{"authors":["admin"],"categories":[],"content":"from IPython.core.display import Image Image(\u0026#39;https://www.python.org/static/community_logos/python-logo-master-v3-TM-flattened.png\u0026#39;) print(\u0026#34;Welcome to Academic!\u0026#34;) Welcome to Academic! Install Python and JupyterLab Install Anaconda which includes Python 3 and JupyterLab.\nAlternatively, install JupyterLab with pip3 install jupyterlab.\nCreate or upload a Jupyter notebook Run the following commands in your Terminal, substituting \u0026lt;MY-WEBSITE-FOLDER\u0026gt; and \u0026lt;SHORT-POST-TITLE\u0026gt; with the file path to your Academic website folder and a short title for your blog post (use hyphens instead of spaces), respectively:\nmkdir -p \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/ cd \u0026lt;MY-WEBSITE-FOLDER\u0026gt;/content/post/\u0026lt;SHORT-POST-TITLE\u0026gt;/ jupyter lab index.ipynb The jupyter command above will launch the JupyterLab editor, allowing us to add Academic metadata and write the content.\nEdit your post metadata The first cell of your Jupter notebook will contain your post metadata (front matter).\nIn Jupter, choose Markdown as the type of the first cell and wrap your Academic metadata in three dashes, indicating that it is YAML front matter:\n--- title: My post\u0026#39;s title date: 2019-09-01 # Put any other Academic metadata here... --- Edit the metadata of your post, using the documentation as a guide to the available options.\nTo set a featured image, place an image named featured into your post’s folder.\nFor other tips, such as using math, see the guide on writing content with Academic.\nConvert notebook to Markdown jupyter nbconvert index.ipynb --to markdown --NbConvertApp.output_files_dir=. Example This post was created with Jupyter. The orginal files can be found at https://github.com/gcushen/hugo-academic/tree/master/exampleSite/content/post/jupyter\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567641600,"objectID":"6e929dc84ed3ef80467b02e64cd2ed64","permalink":"https://ckarag.github.io/post/jupyter/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/post/jupyter/","section":"post","summary":"Learn how to blog in Academic using Jupyter notebooks","tags":[],"title":"Display Jupyter Notebooks with Academic","type":"post"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Wowchemy Wowchemy | Documentation\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026#34;blueberry\u0026#34; if porridge == \u0026#34;blueberry\u0026#34;: print(\u0026#34;Eating...\u0026#34;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne Two Three A fragment can accept two optional parameters:\nclass: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\nOnly the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026#34;/media/boards.jpg\u0026#34; \u0026gt;}} {{\u0026lt; slide background-color=\u0026#34;#0000FF\u0026#34; \u0026gt;}} {{\u0026lt; slide class=\u0026#34;my-style\u0026#34; \u0026gt;}} Custom CSS Example Let’s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://ckarag.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Wowchemy's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":null,"categories":null,"content":"","date":1544313600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1544313600,"objectID":"60552c57f4035741554d5cfde698b784","permalink":"https://ckarag.github.io/project/productivitycy/","publishdate":"2018-12-09T00:00:00Z","relpermalink":"/project/productivitycy/","section":"project","summary":"We estimate labour and total-factor (TFP) productivity for Cyprus, using the growth accounting framework. We then present developments and provide public policy recommendations with the objective of increasing productivity and consequently GDP growth in Cyprus. Visit [CypERC](https://ucyweb.ucy.ac.cy/erc/en/ \"UCY\") for updates.","tags":["Policy"],"title":"Productivity in Cyprus","type":"project"},{"authors":["admin","Robert Ford"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Create your slides in Markdown - click the Slides button to check out the example. Supplementary notes can be added here, including code, math, and images.\n","date":1441065600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1441065600,"objectID":"966884cc0d8ac9e31fab966c4534e973","permalink":"https://ckarag.github.io/publication/journal-article/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/journal-article/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"An example journal article","type":"publication"},{"authors":["admin","Robert Ford"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Create your slides in Markdown - click the Slides button to check out the example. Supplementary notes can be added here, including code, math, and images.\n","date":1372636800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1372636800,"objectID":"69425fb10d4db090cfbd46854715582c","permalink":"https://ckarag.github.io/publication/conference-paper/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/conference-paper/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":[],"title":"An example conference paper","type":"publication"},{"authors":null,"categories":null,"content":"2024 [MATLAB] DFGLS for MATLAB\nDF-GLS unit root test of Elliott, Rothenberg \u0026amp; Stock (1996), with 3 optimal lag-length selection methods (SIC, MAIC, Sequential-t) for selecting the lagged terms in the underlying ADF regression.\nDownload - tutorial\n[MATLAB] The Real-Time Nowcasting Suite\nAn extensive collection of uniformly designed MATLAB functions developed for pre-processing large sets of mixed-frequency data with missing observations at the bottom (ragged-edges) and tailored for forecasting economic indicators in real-time.\nDescription - Download (available shortly) - tutorial (under development)\n2022 [MATLAB] Interface to easily access FRED® data\nDownload - tutorial\n[MATLAB] Collaborator to the MIDAS-ML (MIDAS Sparse-Group LASSO) Package by Jonas Striaukas\nDownload\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"9260a1bd38ad457fcbbc5d4ca2b02951","permalink":"https://ckarag.github.io/codes/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/codes/","section":"","summary":"2024 [MATLAB] DFGLS for MATLAB\nDF-GLS unit root test of Elliott, Rothenberg \u0026 Stock (1996), with 3 optimal lag-length selection methods (SIC, MAIC, Sequential-t) for selecting the lagged terms in the underlying ADF regression.","tags":null,"title":"Codes","type":"page"},{"authors":null,"categories":null,"content":"The $ \\alpha $4casting real-time M$ \\alpha $cro Monitor uses state-of-the-art datasets and statistical models developed exclusively for macroeconomic nowcasting, allowing to closely and accurately monitor economic activity in real-time. The predictive and pre-processing models used to produce our nowcasts and forecasts, follow research that has been published in top academic journals (JBES, Econometrica to name a few) following the scientific developments in predictive macro modelling of the last couple of years. The models are evaluated frequently against their competing alternatives to guarantee leading preformance among the industry. The Macro Monitor avoids human judgment by operating entirely on a large pool of data released by official sources. Data are retreived from the U.S. Census Bureau, U.S. Bureau of Labor Statistics, and other sources. It is fully automated and the predictions are continuously updated in real-time as soon as new data releases become available, allowing to incorporate the latest market and economic developments, hence providing the most up-to-date views for the current and future states of the economies being monitored.\nDisclaimer: The views expressed here are my own and do not reflect those of any institutions I am affiliated with. US The graphs below show the weekly evolution of the nowcasts for the respective period, as models are updated each Friday to reflect newly released information from the preceding week. The realized figures (labelled as ‘Actual’) reflect the revisions from the official source made at the 2 most recent releases (as indicated by the ‘as of’ date in the hover-over text). You can access the spreadsheets containing the latest nowcasts and forecasts as well as the full history of the projections here.\nForecast Dates 2024Q2 2024Q3 2024Q4 28/06/2024 4.33 2.42 2.65 05/07/2024 3.4 4.46 2.64 12/07/2024 3.22 4.78 2.64 19/07/2024 4.1 4.49 2.68 US Real GDP growth Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 3.25 3.17 3.24 05/07/2024 3.46 3.2 3.24 12/07/2024 2.93 2.76 19/07/2024 2.93 2.77 US Headline CPI Inflation Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 2.61 3.18 3.56 05/07/2024 3.75 2.59 3.49 12/07/2024 2.06 2.9 19/07/2024 1.92 2.79 US Core CPI Inflation Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 -0.01 -0.02 0.04 05/07/2024 -0.01 -0.02 12/07/2024 -0.01 -0.02 19/07/2024 -0.01 -0.01 US Unemployment Rate Change Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 0.12 0.11 0.04 05/07/2024 0.13 0.12 12/07/2024 0.12 0.11 19/07/2024 0.1 0.09 US Payroll employment (nonfarm, SA) Forecast Dates Jun-24 Jul-24 Aug-24 28/06/2024 5.47 2.44 2.31 05/07/2024 13.04 10.36 1.85 12/07/2024 13.99 8.97 4.02 19/07/2024 3.86 3.79 US Industrial Production Visualizing the determinants of nowcasts and how those have changed across time. ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"e847d22979794a12a5c43a2eaaf663ef","permalink":"https://ckarag.github.io/rtmonitor/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/rtmonitor/","section":"","summary":"The $ \\alpha $4casting real-time M$ \\alpha $cro Monitor uses state-of-the-art datasets and statistical models developed exclusively for macroeconomic nowcasting, allowing to closely and accurately monitor economic activity in real-time. The predictive and pre-processing models used to produce our nowcasts and forecasts, follow research that has been published in top academic journals (JBES, Econometrica to name a few) following the scientific developments in predictive macro modelling of the last couple of years.","tags":null,"title":"M$ \\alpha $cro Monitor","type":"page"},{"authors":null,"categories":null,"content":"Nowcasting with State-of-the-Art Methodologies The purpose of the nowcasts in this section is not to give a single number, rather it is to give an indication of how well the latest research published in top academic journals, and highly cited methodologies for nowcasting, perform on a real-time basis.\nThe 1st two plots contain nowcasts based on two state-of-the-art nowcasting methodologies. The two methodologies are the Factor-augmented AR (FAR) methodology of Stock-Watson (2002), and the Sg-LASSO-MIDAS by Babii et al. (2022). The nowcasts at the last plot are based on the tutorial material I am teaching for the MSc course titled ‘Intro to Big Data Analytics’ at KCL. In the two plots at the top, all the simplifications made in the course are dropped.\nThe dataset is made of 160 carefully selected mixed-frequency indicators, that are updated on a timely basis (i.e. every time the nowcasts are re-run). As such, the nowcasts reflect the information contained in the latest released economic and market data, as of the day of the estimation (which can be seen by hovering over the corresponding points in the plots). The mixed-frequency panel of predictors contains weekly, daily, and monthly indicators. The series that is nowcasted is the annualized MoM% headline CPI for the US (FRED mnemonic: CPIAUCSL).\nSOTA Nowcasts\nMSc Nowcasts\nReferences Babii, A., Ghysels E., \u0026amp; Striaukas, J. (2022). “Machine learning time series regressions with an application to nowcasting.” Journal of Business \u0026amp; Economic Statistics, 40(3), 1094-1106. Stock, J. H., \u0026amp; Watson, M. W. (2002). “Macroeconomic forecasting using diffusion indexes.” Journal of Business \u0026amp; Economic Statistics, 20(2), 147-162. ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"d2f3737e447a3be56cf02f948e029668","permalink":"https://ckarag.github.io/sota/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/sota/","section":"","summary":"Nowcasting with State-of-the-Art Methodologies The purpose of the nowcasts in this section is not to give a single number, rather it is to give an indication of how well the latest research published in top academic journals, and highly cited methodologies for nowcasting, perform on a real-time basis.","tags":null,"title":"Macro Nowcasting","type":"page"},{"authors":null,"categories":null,"content":"List of forecasting models Acronym Model Description AR(P) Autoregressive iterated-specification RW Random walk ARDI(K,BIC) Autoregressive diffusion indices with K factors. Optimal lag-order via BIC T.ARDI(K,BIC) ARDI with target-factors. Hard-threshold set to |t-stat|\u0026gt;1.96 BVAR-Minn Homoscedastic large Bayesian VAR BVAR-CSV Large Bayesian VAR with heteroscedastic innovations BBoost Quadratic-loss L2 boosting, block-wise CBoost Quadratic-loss L2 boosting, component-wise CSR Complete Subset Regressions (20C4) with hard-thresholding preselection Bag Linear bagging with hard-thresholding preselection BTree Boosting regression trees RF Random forests SVR Support vector machine regression with Gaussian Kernel function Ridge Ridge regression LASSO Least absolute shrinkage and selection operator with BIC for lambda AdaLASSO Adaptive LASSO EN Elastic Net AdaEN Adaptive EN LSTM Long-short-term memory RNN with 3-hidden layers SgLASSO Sparse-group-LASSO-MIDAS with block-K-fold CV for lambda \u0026amp; alpha List of methods for treating mixed-frequencies Acronym Transformation Description D1 Down-sampling to Quarterly (Temporal aggregation with equal-weights) D2 Unrestricted MIDAS Polynomials D3 Legendre Polynomials (3rd degree) The Ranking: Real-time model evaluation Models n=0 n=1 n=2 n=3 n=4 avg Bag-D3 0.728 0.932 0.972 0.998 0.994 0.925 CSR-D2 0.707 0.938 0.976 1 1.004 0.925 BBoost-D1F 0.725 0.925 0.978 1.006 1.001 0.927 Bag-D2F 0.772 0.91 0.967 0.994 0.999 0.929 Ridge-D1 0.764 0.924 0.958 0.996 1.001 0.929 Ridge-D2 0.742 0.926 0.972 1.001 1.005 0.929 Bag-D2 0.767 0.921 0.969 0.993 1.002 0.93 T.ARDI(2) 0.745 0.929 0.975 1.002 1.007 0.932 Bag-D1F 0.759 0.925 0.97 1.005 1.001 0.932 Bag-D1 0.783 0.918 0.961 0.996 1.002 0.932 CSR-D1 0.758 0.934 0.974 1.004 1.009 0.936 Ridge-D3 0.791 0.921 0.969 1.004 1.003 0.938 RF-D1F 0.816 0.917 0.966 0.995 0.992 0.938 Ridge-D2F 0.808 0.913 0.968 0.999 1.004 0.938 CBoost-D1F 0.773 0.925 0.983 1.01 1.001 0.938 SVR-D1 0.825 0.919 0.964 0.992 0.995 0.939 LASSO-D1F 0.787 0.93 0.978 1.002 0.999 0.939 AdaLASSO-D1F 0.781 0.937 0.98 1.003 0.998 0.94 RF-D1 0.804 0.924 0.971 0.999 1.003 0.94 Bag-D3F 0.816 0.917 0.972 1.005 0.993 0.941 EN-D2F 0.816 0.921 0.969 0.999 1.001 0.941 EN-D1F 0.799 0.928 0.978 1.001 1 0.941 SVR-D3 0.827 0.92 0.97 0.995 0.997 0.942 RF-D3 0.812 0.936 0.969 0.994 1.001 0.942 ARDI(1) 0.799 0.939 0.975 0.999 1.001 0.943 CBoost-D2F 0.821 0.92 0.965 0.999 1.008 0.943 BBoost-D2F 0.836 0.912 0.967 0.998 1 0.943 SVR-D3F 0.831 0.914 0.972 0.995 1.001 0.943 SVR-D2 0.828 0.923 0.971 0.997 0.997 0.943 AdaEN-D2 0.766 0.947 0.977 1.003 1.024 0.943 EN-D2 0.777 0.971 0.978 0.996 0.994 0.943 RF-D2 0.816 0.925 0.976 1.001 1 0.943 LASSO-D2F 0.831 0.92 0.969 0.998 1 0.944 AdaEN-D1F 0.802 0.934 0.981 1.002 1 0.944 SVR-D1F 0.827 0.921 0.971 0.999 1 0.944 CSR-D2F 0.824 0.928 0.97 0.997 1 0.944 SVR-D2F 0.831 0.925 0.972 0.996 0.995 0.944 RF-D2F 0.827 0.929 0.972 0.998 0.995 0.944 BTree-D1F 0.811 0.923 0.975 1.002 1.008 0.944 AdaEN-D2F 0.834 0.926 0.965 0.999 1.002 0.945 AdaLASSO-D2F 0.84 0.922 0.966 0.998 1.001 0.945 AdaLASSO-D2 0.765 0.954 0.979 0.998 1.034 0.946 LSTM-D1F 0.845 0.918 0.968 1.005 0.994 0.946 ARDI(2) 0.798 0.947 0.978 1.001 1.008 0.946 EN-D3 0.834 0.924 0.975 1 1 0.947 CSR-D3 0.814 0.931 0.98 1.005 1.003 0.947 RW 0.839 0.921 0.975 0.999 0.999 0.947 LSTM-D2F 0.844 0.928 0.966 1 0.997 0.947 LASSO-D2 0.78 0.98 0.983 1 0.997 0.948 BVAR-CSV 0.811 0.926 0.98 1.012 1.013 0.948 RF-D3F 0.844 0.934 0.976 0.991 1.001 0.949 T.ARDI(1) 0.814 0.948 0.975 1.005 1.007 0.95 AdaEN-D1 0.817 0.931 0.991 1.007 1.003 0.95 BBoost-D3F 0.865 0.906 0.973 1.006 1.001 0.95 AdaEN-D3 0.848 0.928 0.976 1 1.002 0.951 LSTM-D3F 0.851 0.926 0.972 0.994 1.013 0.951 SgLASSO-D3 0.849 0.925 0.978 1.003 1.005 0.952 SgLASSO-D3F 0.861 0.912 0.977 1.011 1.003 0.953 CSR-D1F 0.877 0.925 0.97 0.998 0.999 0.954 EN-D1 0.809 0.936 0.944 1.045 1.036 0.954 BTree-D1 0.812 0.933 0.988 1.015 1.024 0.955 AdaEN-D3F 0.883 0.924 0.968 1.001 0.999 0.955 LSTM-D3 0.858 0.932 0.978 1.005 1.004 0.955 BVAR-Minn 0.85 0.933 0.974 1.012 1.015 0.957 EN-D3F 0.891 0.926 0.971 1 0.999 0.957 CBoost-D3F 0.854 0.936 0.985 1.013 1.001 0.958 LSTM-D1 0.866 0.936 0.978 1.002 1.013 0.959 CSR-D3F 0.89 0.938 0.97 1.001 0.997 0.959 LASSO-D3F 0.903 0.921 0.971 1 1.003 0.96 AdaLASSO-D3F 0.893 0.929 0.971 1.003 1.004 0.96 BTree-D2F 0.85 0.954 0.991 1.017 0.997 0.962 LSTM-D2 0.868 0.943 0.986 1.017 0.999 0.962 BTree-D2 0.839 0.952 0.994 1.012 1.016 0.963 Ridge-D3F 0.802 0.941 1.017 1.046 1.033 0.968 AdaLASSO-D1 0.83 0.947 1.019 1.037 1.022 0.971 BTree-D3 0.851 0.988 0.99 1.016 1.021 0.973 Ridge-D1F 0.779 0.959 1.026 1.06 1.054 0.976 LASSO-D3 0.832 0.932 1.018 1.06 1.056 0.98 AdaLASSO-D3 0.834 0.945 1.03 1.065 1.06 0.987 LASSO-D1 0.774 1.007 0.987 1.102 1.074 0.989 BTree-D3F 0.88 1.031 0.999 1.01 1.033 0.991 AR(BIC) 1.008 1.003 1 1.001 1 1.002 AR(CV) 1.02 1.004 1 1.001 0.999 1.005 AR(4) 1.049 1.017 1.002 0.999 0.999 1.013 AR(1) NaN NaN NaN NaN NaN NaN ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"6d267b6bb6f97459260d2aef4d0f4b62","permalink":"https://ckarag.github.io/bmnr/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/bmnr/","section":"","summary":"List of forecasting models Acronym Model Description AR(P) Autoregressive iterated-specification RW Random walk ARDI(K,BIC) Autoregressive diffusion indices with K factors. Optimal lag-order via BIC T.ARDI(K,BIC) ARDI with target-factors. Hard-threshold set to |t-stat|\u003e1.","tags":null,"title":"The Big Macro Nowcasting Ranking","type":"page"}] \ No newline at end of file diff --git a/index.xml b/index.xml index c48b7230..27fbad69 100644 --- a/index.xml +++ b/index.xml @@ -229,7 +229,7 @@ console.log('code block'); <p>Wowchemy supports the popular <a href="https://plot.ly/" target="_blank" rel="noopener">Plotly</a> format for interactive charts.</p> <p>Save your Plotly JSON in your page folder, for example <code>line-chart.json</code>, and then add the <code>{{&lt; chart data=&quot;line-chart&quot; &gt;}}</code> shortcode where you would like the chart to appear.</p> <p>Demo:</p> -<div id="chart-653872941" class="chart"></div> +<div id="chart-873154269" class="chart"></div> <script> (function() { let a = setInterval( function() { @@ -238,7 +238,7 @@ return; } clearInterval( a ); Plotly.d3.json("./line-chart.json", function(chart) { -Plotly.plot('chart-653872941', chart.data, chart.layout, {responsive: true}); +Plotly.plot('chart-873154269', chart.data, chart.layout, {responsive: true}); }); }, 500 ); })(); @@ -648,6 +648,7 @@ An extensive collection of uniformly designed MATLAB functions developed for pre </ul>M$ \alpha $cro Monitorhttps://ckarag.github.io/rtmonitor/Mon, 01 Jan 0001 00:00:00 +0000https://ckarag.github.io/rtmonitor/<p>The $ \alpha $4casting real-time M$ \alpha $cro Monitor uses state-of-the-art datasets and statistical models developed exclusively for macroeconomic nowcasting, allowing to closely and accurately monitor economic activity in real-time. The predictive and pre-processing models used to produce our nowcasts and forecasts, follow research that has been published in top academic journals (JBES, Econometrica to name a few) following the scientific developments in predictive macro modelling of the last couple of years. The models are evaluated frequently against their competing alternatives to guarantee leading preformance among the industry. The Macro Monitor avoids human judgment by operating entirely on a large pool of data released by official sources. Data are retreived from the U.S. Census Bureau, U.S. Bureau of Labor Statistics, and other sources. It is fully automated and the predictions are continuously updated in real-time as soon as new data releases become available, allowing to incorporate the latest market and economic developments, hence providing the most up-to-date views for the current and future states of the economies being monitored.</p> +<p><font size=”1”> <em>Disclaimer: The views expressed here are my own and do not reflect those of any institutions I am affiliated with.</em> </font></p> <h3 id="us">US</h3> <p>The graphs below show the weekly evolution of the nowcasts for the respective period, as models are updated each Friday to reflect newly released information from the preceding week. The realized figures (labelled as ‘Actual’) reflect the revisions from the official source made at the 2 most recent releases (as indicated by the ‘as of’ date in the hover-over text). You can access the spreadsheets containing the latest nowcasts and forecasts as well as the full history of the projections <a href="https://ckarag.github.io/uploads/US_d0.xlsx" target="_blank" rel="noopener">here</a>.</p> <iframe width="700" height="600" frameborder="0" scrolling="no" src="//plotly.com/~ckara/89.embed?show_link=false"></iframe> @@ -832,8 +833,7 @@ src="https://ckarag.github.io/MLinterpret_CPI.gif" width="800" height="550" style="border:none;"> -</iframe> -<p><font size=”1”> <em>Disclaimer: The views expressed here are my own and do not reflect those of any institutions I am affiliated with.</em> </font></p>Macro Nowcastinghttps://ckarag.github.io/sota/Mon, 01 Jan 0001 00:00:00 +0000https://ckarag.github.io/sota/<h3 id="nowcasting-with-state-of-the-art-methodologies">Nowcasting with State-of-the-Art Methodologies</h3> +</iframe>Macro Nowcastinghttps://ckarag.github.io/sota/Mon, 01 Jan 0001 00:00:00 +0000https://ckarag.github.io/sota/<h3 id="nowcasting-with-state-of-the-art-methodologies">Nowcasting with State-of-the-Art Methodologies</h3> <p>The purpose of the nowcasts in this section is not to give a single number, rather it is to give an indication of how well the latest research published in top academic journals, and highly cited methodologies for nowcasting, perform on a real-time basis.</p> <p>The 1st two plots contain nowcasts based on two state-of-the-art nowcasting methodologies. The two methodologies are the <em>Factor-augmented AR (FAR)</em> methodology of Stock-Watson (2002), and the <em>Sg-LASSO-MIDAS</em> by Babii et al. (2022). The nowcasts at the last plot are based on the tutorial material I am teaching for the MSc course titled ‘Intro to Big Data Analytics’ at KCL. In the two plots at the top, all the simplifications made in the course are dropped.</p> <p>The dataset is made of 160 carefully selected mixed-frequency indicators, that are updated on a timely basis (i.e. every time the nowcasts are re-run). As such, the nowcasts reflect the information contained in the latest released economic and market data, as of the day of the estimation (which can be seen by hovering over the corresponding points in the plots). The mixed-frequency panel of predictors contains weekly, daily, and monthly indicators. The series that is nowcasted is the annualized MoM% headline CPI for the US (FRED mnemonic: <a href="https://fred.stlouisfed.org/series/CPIAUCSL" target="_blank" rel="noopener">CPIAUCSL</a>).</p> diff --git a/post/index.xml b/post/index.xml index 20e2ac16..dfcd0ec1 100644 --- a/post/index.xml +++ b/post/index.xml @@ -145,7 +145,7 @@ console.log('code block'); <p>Wowchemy supports the popular <a href="https://plot.ly/" target="_blank" rel="noopener">Plotly</a> format for interactive charts.</p> <p>Save your Plotly JSON in your page folder, for example <code>line-chart.json</code>, and then add the <code>{{&lt; chart data=&quot;line-chart&quot; &gt;}}</code> shortcode where you would like the chart to appear.</p> <p>Demo:</p> -<div id="chart-653872941" class="chart"></div> +<div id="chart-873154269" class="chart"></div> <script> (function() { let a = setInterval( function() { @@ -154,7 +154,7 @@ return; } clearInterval( a ); Plotly.d3.json("./line-chart.json", function(chart) { -Plotly.plot('chart-653872941', chart.data, chart.layout, {responsive: true}); +Plotly.plot('chart-873154269', chart.data, chart.layout, {responsive: true}); }); }, 500 ); })(); diff --git a/post/writing-technical-content/index.html b/post/writing-technical-content/index.html index 4b9cdda3..5998aafa 100644 --- a/post/writing-technical-content/index.html +++ b/post/writing-technical-content/index.html @@ -68,7 +68,7 @@ console.log('hello'); console.log('code block'); ``` - - Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Charts

Wowchemy supports the popular Plotly format for interactive charts.

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the Plotly JSON Editor useful.

Math

Wowchemy supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively. (We wrap the LaTeX math in the Wowchemy math shortcode to prevent Hugo rendering our math as Markdown. The math shortcode is new in v5.5-dev.)

Example math block:

{{< math >}}
+    - Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Charts

Wowchemy supports the popular Plotly format for interactive charts.

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the Plotly JSON Editor useful.

Math

Wowchemy supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively. (We wrap the LaTeX math in the Wowchemy math shortcode to prevent Hugo rendering our math as Markdown. The math shortcode is new in v5.5-dev.)

Example math block:

{{< math >}}
 $$
 \gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
 $$
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 Dark
 Automatic

M$ \alpha $cro Monitor

The $ \alpha $4casting real-time M$ \alpha $cro Monitor uses state-of-the-art datasets and statistical models developed exclusively for macroeconomic nowcasting, allowing to closely and accurately monitor economic activity in real-time. The predictive and pre-processing models used to produce our nowcasts and forecasts, follow research that has been published in top academic journals (JBES, Econometrica to name a few) following the scientific developments in predictive macro modelling of the last couple of years. The models are evaluated frequently against their competing alternatives to guarantee leading preformance among the industry. -The Macro Monitor avoids human judgment by operating entirely on a large pool of data released by official sources. Data are retreived from the U.S. Census Bureau, U.S. Bureau of Labor Statistics, and other sources. It is fully automated and the predictions are continuously updated in real-time as soon as new data releases become available, allowing to incorporate the latest market and economic developments, hence providing the most up-to-date views for the current and future states of the economies being monitored.

US

The graphs below show the weekly evolution of the nowcasts for the respective period, as models are updated each Friday to reflect newly released information from the preceding week. The realized figures (labelled as ‘Actual’) reflect the revisions from the official source made at the 2 most recent releases (as indicated by the ‘as of’ date in the hover-over text). You can access the spreadsheets containing the latest nowcasts and forecasts as well as the full history of the projections here.

Forecast Dates2024Q22024Q32024Q4
28/06/20244.332.422.65
05/07/20243.44.462.64
12/07/20243.224.782.64
19/07/20244.14.492.68
US Real GDP growth
Forecast DatesJun-24Jul-24Aug-24
28/06/20243.253.173.24
05/07/20243.463.23.24
12/07/20242.932.76
19/07/20242.932.77
US Headline CPI Inflation
Forecast DatesJun-24Jul-24Aug-24
28/06/20242.613.183.56
05/07/20243.752.593.49
12/07/20242.062.9
19/07/20241.922.79
US Core CPI Inflation
Forecast DatesJun-24Jul-24Aug-24
28/06/2024-0.01-0.020.04
05/07/2024-0.01-0.02
12/07/2024-0.01-0.02
19/07/2024-0.01-0.01
US Unemployment Rate Change
Forecast DatesJun-24Jul-24Aug-24
28/06/20240.120.110.04
05/07/20240.130.12
12/07/20240.120.11
19/07/20240.10.09
US Payroll employment (nonfarm, SA)
Forecast DatesJun-24Jul-24Aug-24
28/06/20245.472.442.31
05/07/202413.0410.361.85
12/07/202413.998.974.02
19/07/20243.863.79
US Industrial Production
  • Visualizing the determinants of nowcasts and how those have changed across time.

Disclaimer: The views expressed here are my own and do not reflect those of any institutions I am affiliated with.

+The Macro Monitor avoids human judgment by operating entirely on a large pool of data released by official sources. Data are retreived from the U.S. Census Bureau, U.S. Bureau of Labor Statistics, and other sources. It is fully automated and the predictions are continuously updated in real-time as soon as new data releases become available, allowing to incorporate the latest market and economic developments, hence providing the most up-to-date views for the current and future states of the economies being monitored.

Disclaimer: The views expressed here are my own and do not reflect those of any institutions I am affiliated with.

US

The graphs below show the weekly evolution of the nowcasts for the respective period, as models are updated each Friday to reflect newly released information from the preceding week. The realized figures (labelled as ‘Actual’) reflect the revisions from the official source made at the 2 most recent releases (as indicated by the ‘as of’ date in the hover-over text). You can access the spreadsheets containing the latest nowcasts and forecasts as well as the full history of the projections here.

Forecast Dates2024Q22024Q32024Q4
28/06/20244.332.422.65
05/07/20243.44.462.64
12/07/20243.224.782.64
19/07/20244.14.492.68
US Real GDP growth
Forecast DatesJun-24Jul-24Aug-24
28/06/20243.253.173.24
05/07/20243.463.23.24
12/07/20242.932.76
19/07/20242.932.77
US Headline CPI Inflation
Forecast DatesJun-24Jul-24Aug-24
28/06/20242.613.183.56
05/07/20243.752.593.49
12/07/20242.062.9
19/07/20241.922.79
US Core CPI Inflation
Forecast DatesJun-24Jul-24Aug-24
28/06/2024-0.01-0.020.04
05/07/2024-0.01-0.02
12/07/2024-0.01-0.02
19/07/2024-0.01-0.01
US Unemployment Rate Change
Forecast DatesJun-24Jul-24Aug-24
28/06/20240.120.110.04
05/07/20240.130.12
12/07/20240.120.11
19/07/20240.10.09
US Payroll employment (nonfarm, SA)
Forecast DatesJun-24Jul-24Aug-24
28/06/20245.472.442.31
05/07/202413.0410.361.85
12/07/202413.998.974.02
19/07/20243.863.79
US Industrial Production
  • Visualizing the determinants of nowcasts and how those have changed across time.