Unit Root Tests and Differencing in R

Overview

This repository contains three R functions for handling unit root tests in time series data:

1. adf_test: Performs the Augmented Dickey-Fuller (ADF) test to check for stationarity in time series. If non-stationary series are detected, the test is automatically applied to their first differences.
2. remove_unit_root: Sequentially differences non-stationary variables until stationarity is achieved or a maximum number of differences is reached.
3. panel_unit_root_tests: Conducts panel unit root tests (Maddala and Wu, Choi, and Levin-Lin-Chu) on panel data.

Why Use These Functions?

These functions automate the process of checking for unit roots and transforming data to stationarity, saving time and effort when working with large datasets. They:

• Apply the ADF test efficiently to multiple time series variables.
• Automatically identify and handle non-stationary series.
• Perform sequential differencing without manual intervention.
• Provide clear outputs, making it easy to track transformations.
• Conduct panel unit root tests to handle panel data.

Installation

Ensure you have the required R packages installed before using the functions:

install.packages(c("urca", "dplyr", "purrr", "tidyr", "tibble", "plm"))

Usage

Augmented Dickey-Fuller (ADF) Test

# Load the functions
source("adf_test.R")

# Example: Run ADF test on a data frame
results <- adf_test(data)

# Access results
print(results)

Removing Unit Roots with Sequential Differencing

# Load the function
source("remove_unit_root.R")

# Example: Apply sequential differencing
result <- remove_unit_root(data, max_diff = 2)

# Transformed data
transformed_data <- result$data

# Check differencing history
print(result$control)

Panel Unit Root Tests

# Load the function
source("panel_unit_root_tests.R")

# Example: Conduct panel unit root tests
results <- panel_unit_root_tests(panel_data)

# Print summary of results
results$summary()

Function Details

adf_test(data, type = "drift")

• Description: Runs the Augmented Dickey-Fuller (ADF) test for stationarity on all numeric columns in a data frame.
• Parameters:
  • data: A data frame with time series variables.
  • type: Type of ADF test ("none", "drift", or "trend"). Default is "drift".
• Returns:
  • If all series are stationary: A data frame with test results.
  • If non-stationary series are detected: A list with results for level and first difference tests.

remove_unit_root(data, max_diff = 3)

• Description: Applies sequential differencing to non-stationary variables.
• Parameters:
  • data: A data frame with time series variables.
  • max_diff: Maximum number of differences to apply (default = 3).
• Returns: A list with transformed data and a control table tracking the differencing process.

panel_unit_root_tests(panel_data)

• Description: Conducts panel unit root tests (Maddala and Wu, Choi, and Levin-Lin-Chu) on panel data.

• Parameters:

  • data: A data frame or matrix where rows represent time periods and columns represent individual units (e.g., countries, firms).

• Returns: A list containing the results of the Maddala and Wu test (mw), Choi test (choi), and Levin-Lin-Chu test (llc). Each element is an object of class "htest" with components:

  • statistic: The value of the test statistic.
  • parameter: The degrees of freedom for the test statistic.
  • p.value: The p-value for the test.
  • method: A character string indicating the type of test performed.
  • data.name: A character string giving the name of the data.
  • Additionally, the list includes a summary function to print the test results.

Contributions

Feel free to open issues or submit pull requests for improvements!