diff --git a/bmnr/index.html b/bmnr/index.html index fe15e3d7..0bf8e3e4 100644 --- a/bmnr/index.html +++ b/bmnr/index.html @@ -1,9 +1,9 @@ -The Big Macro Nowcasting Ranking | Haris Karagiannakis

The Big Macro Nowcasting Ranking

US

  • Performance evaluation exercise with monthly (pseudo) real-time vintages of a mixed-frequency large dataset for the US. The target variable is real GDP growth. Reported figures are RMSE relative to the AR(1). The horizon denotes quarters-ahead. D1 denots single-frequency information set; D2 mixed-frequency. The monthly vintages can be accessed here.
Modelsn=0n=1n=2n=3n=4avg
Bag-D30.7283321923312020.9320147952584180.9716475130734890.9980124233019070.9943874266656680.924878870126137
CSR-D20.7074672186654570.938090660705060.9763379809510851.000219498304481.004341589821090.925291389689434
BBoost-D1F0.7245996123685210.9246299881700190.9775408062557681.005853056522581.001389845008820.92680266166514
Bag-D2F0.7720824217291860.9102409317538020.9671543344914860.9943717426278460.9987520347871060.928520293077885
Ridge-D10.7644529595768080.9240328080480140.958256111618330.9960783989388691.000937894649890.928751634566381
Ridge-D20.7417665491192860.9263936462781020.9718531452252671.000767897402531.00482479213590.929121206032218
Bag-D20.7665428761394990.9212912146040810.9687018350705450.9934578626759291.001960479163720.930390853530756
T.ARDI(2)0.745183817570440.929309181212520.9754020921419441.002491923830171.006563050838480.931790013118709
Bag-D1F0.7590940346142020.9253269673738820.9696644825381321.004590853763061.000606959195120.931856659496879
Bag-D10.7825981119955610.9175835951059180.9613786822465830.9962161956884811.002106165094780.931976550026265
CSR-D10.758100170901410.9338816379320260.9740675317702351.004359167285131.00855130276220.9357919621302
Ridge-D30.7907717826502940.9212569329571520.9687204977232391.004422836400121.002580446500970.937550499246356
RF-D1F0.8164666115027630.9172359000267280.9662966995542910.9954830144813460.9923265936723870.937561763847503
Ridge-D2F0.8077449782292810.912665211549450.9675335486038040.9991931585388891.003565212854720.938140421955228
CBoost-D1F0.7733236834352380.9251812619237680.982889790000871.009683123671581.001107080875450.938436987981382
SVR-D10.8247464524348390.9192776014675680.9639999858656050.9916981328524860.9950941089632130.938963256316742
LASSO-D1F0.7874069769472660.9299106394198980.9779232501862091.001640745499190.9986939467643090.939115111763374
AdaLASSO-D1F0.7812044869418750.9365494246873950.9800467812479791.003359358099040.997978254917860.93982766117883
RF-D10.804380094949980.9239377109107930.9706874528572360.9990467727964291.002979553572990.940206317017486
Bag-D3F0.8161000842897830.9167951783244620.972289299886431.004743600920310.9927091612106420.940527464926325
EN-D2F0.8164643650488410.9209093627920390.9687427866772240.9991034636691061.000791932327980.941202382103038
EN-D1F0.7992850661260950.9275027440009680.9782331774339031.00106011719381.000033908102630.941223002571478
SVR-D30.8272219350906540.9197654774616360.9696888049679610.9947150309562180.9973304314801950.941744335991333
RF-D30.8123126365028690.9356316413829580.9692474674399630.9937720696683221.000935914489930.942379945896809
ARDI(1)0.799345138861390.9393421788690880.9745692908098950.9988497043362681.000817393312350.942584741237798
CBoost-D2F0.8214541542859970.9198056440406880.9653024404609290.9988489395569241.008078759181370.942697987505181
BBoost-D2F0.8364163343141330.9115709587428310.9674103913535160.9980131293781961.000171991538880.94271656106551
SVR-D3F0.8310591950278360.9143745997336840.9722716804808140.995200058488191.001254942650.942832095276105
SVR-D20.8277974318988460.92316648744980.9707192867601490.99657185412160.9970704818176290.943065108409605
AdaEN-D20.7658258300545280.9468658289022260.9765260459927491.002675354415271.024038016231430.94318621511924
EN-D20.7766329053367910.9712056537873190.9779305991856360.9963689121634740.9942564843371580.943278910962075
RF-D20.815595275238120.9252604973841210.9758223048571121.000660156862941.000021842066590.943472015281775
LASSO-D2F0.8312646010414640.9200221424550460.9691808779218690.9976400990286260.9995719008875550.943535924266912
AdaEN-D1F0.8021346204611850.9335045314950690.9810321522958921.00205415434030.9996447297361250.943674037665713
SVR-D1F0.8268625599104460.921184492297870.9714618183206420.9985687191034041.000488362237920.943713190374057
CSR-D2F0.8240881499647110.9281002785868290.9700088130619110.9970936583744150.9998885016671030.943835880330994
SVR-D2F0.8311296692705940.924575065465860.9722285603002110.9960559378383910.9952818820532780.943854222985667
RF-D2F0.8269876184388050.9289142173732980.9716744637425160.997740448740090.9947878760567650.944020924870295
BTree-D1F0.8113126118762650.9233049857245090.9752641428854231.002479006460891.007918857223590.944055920834134
AdaEN-D2F0.8335553195258170.9262694135061210.9648291169328560.9989400871738671.001935100915150.945105807610762
AdaLASSO-D2F0.8397857301322180.9222382134608470.9656243030792360.9983063440279351.001056346637810.94540218746761
AdaLASSO-D20.7648986639314140.9541190886947570.9788434048892480.9978263978514491.033950497263530.945927610526079
LSTM-D1F0.845431866155280.9176202224270290.9679199372829821.004985467888760.9937756427808270.945946627306976
ARDI(2)0.7981758468012860.9468175097935160.9779075212014551.0009139545371.008359565017270.946434879470105
EN-D30.8337750805615250.9235163556380860.9751457402344040.9997409949672581.000470150733650.946529664426985
CSR-D30.8138375461108640.9314094767653560.9802949352918711.004575253393051.00277147849320.946577738010869
RW0.8385817248079540.9212251363023310.9750670164836390.9994347822544170.9994761660702260.946756965183713
LSTM-D2F0.8444201638004680.928055565663170.9660506188157340.9995977199468710.9967608975769030.946976993160629
LASSO-D20.7801683124295940.9796046820338330.9829572169810920.9996316509885950.9966036887532860.94779311023728
BVAR-CSV0.8107804824686630.9255366679613370.9798301773384851.011610835545331.012865958024440.948124824267652
RF-D3F0.8444918126220790.9341346201319650.9758432527956740.9909479944840551.001050133677660.949293562742288
T.ARDI(1)0.8137676316224940.9483183052974320.97509831379381.004656069343011.006798787302080.949727821471763
AdaEN-D10.8166583399020440.9306466078645290.9914261420086521.007290871469331.002685849566790.94974156216227
BBoost-D3F0.8653360723596710.9062124228804810.9725479243798951.005506752324731.001486666530850.950217967695126
AdaEN-D30.8477269060543470.9277487963928190.9755347232258690.9997370274001921.001754911240030.950500472862651
LSTM-D3F0.8511678701147290.9263202783050510.9721430200388680.993708544811731.012653504582470.95119864357057
SgLASSO-D30.8490489051878280.9245160335564060.9776885764978451.003239233925651.004846166515450.951867783136636
SgLASSO-D3F0.8606118946478910.9118994694458980.9767728365078641.011036151215691.003322283856090.952728527134687
CSR-D1F0.8765638397480160.924830962012050.9704921426782550.998253943794910.9985761732338210.953743412293411
EN-D10.8089015546928120.935948086981530.9440616114452341.044547748779611.035587241853530.953809248750542
BTree-D10.8119286899796580.9332022747072670.9881098533419281.014919292600131.024413091115480.954514640348893
AdaEN-D3F0.882624588688150.9238619078688040.9682250264964941.001028227665710.9990257588213970.954953101908112
LSTM-D30.8582607717347830.931970035382440.9775150343966161.005323707450531.003519101167040.955317730026282
BVAR-Minn0.8500561919445530.9331021182892950.9737548332089031.012394030744731.014580791471390.956777593131774
EN-D3F0.8909987665369110.925793688341460.9711854412414130.9999061402782630.9994396385732190.957464734994253
CBoost-D3F0.8538839939888420.936055620826670.98458333045441.013044782132841.000890930687680.957691731618088
LSTM-D10.8661139361432620.9359672810150550.977786381964561.00210981607061.013402920086760.959076067056048
CSR-D3F0.8898269278943860.9379283575559420.9704078609002951.00137096736370.996932157332810.959293254209426
LASSO-D3F0.9031702263330110.9214526556384210.9712754334872641.000384890826411.003204617896810.959897564836382
AdaLASSO-D3F0.8932530080707580.9290493735976040.9710498115063191.002530333529951.003827129892230.959941931319374
BTree-D2F0.8499921369333110.9539525723155580.991447739941621.017252471473620.996959896716850.961920963476192
LSTM-D20.867691421895970.9431900090056040.9855075391024151.016898338561560.9989949188384020.962456445480791
BTree-D20.839015073630160.9517916924650790.994161006594911.011956926102851.016289608918390.962642861542278
Ridge-D3F0.8023245557137550.9405140450160481.017490325047861.046364801930931.032573079410660.967853361423849
AdaLASSO-D10.8295413792410730.9466595364242661.019227760421621.037446112588151.022338841081720.971042725951366
BTree-D30.8514099582613560.9878190575296270.990238828103921.016135568304271.021289390162790.973378560472392
Ridge-D1F0.7785458427631710.959448932708721.02582121916221.060077588246381.053752808634340.975529278302963
LASSO-D30.8318772240200840.932179439362341.01825147744951.060045084080441.056345034110340.97973965180454
AdaLASSO-D30.8338118960560040.9452929916561751.030469197359181.064919746114691.059729287444550.98684462372612
LASSO-D10.7743171794544631.006875995555640.9871502554486931.101898396998881.07355573424540.988759512340616
BTree-D3F0.8795893788126931.030651280935820.9991460294616571.010333611761941.032811822674130.990506424729248
AR(BIC)1.007916379020091.003251532599911.000400415366441.00083227205810.9997281797117531.00242575575126
AR(CV)1.019707806621811.003732641542071.000341283998591.001288572197090.9993462137491741.00488330362175
AR(4)1.049297819160481.016680055792261.001635153982490.9993195915137280.9991650187192161.01321952783363
AR(1)NaNNaNNaNNaNNaNNaN
Table: ML Results
+Automatic

The Big Macro Nowcasting Ranking

  • Performance evaluation exercise with monthly (pseudo) real-time vintages of a mixed-frequency large dataset for the US. The target variable is real GDP growth. Reported figures are RMSE relative to the AR(1). The horizon denotes quarters-ahead. D1 denots single-frequency information set; D2 mixed-frequency. The monthly vintages can be accessed here.

List of forecasting models & transformations

  • List of time series forecasting models
    AcronymModel Description
    AR(P)Autoregressive iterated-specification
    RWRandom 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|>1.96
    BVAR-MinnHomoscedastic large Bayesian VAR
    BVAR-CSVLarge Bayesian VAR with heteroscedastic innovations
    BBoostQuadratic-loss L2 boosting, block-wise
    CBoostQuadratic-loss L2 boosting, component-wise
    CSRComplete Subset Regressions (20C4) with hard-thresholding preselection
    BagLinear bagging with hard-thresholding preselection
    BTreeBoosting regression trees
    RFRandom forests
    SVRSupport vector machine regression with Gaussian Kernel function
    RidgeRidge regression
    LASSOLeast absolute shrinkage and selection operator with BIC for lambda
    AdaLASSOAdaptive LASSO
    ENElastic Net
    AdaENAdaptive EN
    LSTMLong-short-term memory RNN with 3-hidden layers
    SgLASSOSparse-group-LASSO-MIDAS with block-K-fold CV for lambda & alpha

  • List of methods for treating mixed-frequencies
    AcronymTransformation Description
    D1Down-sampling to Quarterly (Temporal aggregation with equal-weights)
    D2Unrestricted MIDAS Polynomials
    D3Legendre Polynomials (3rd degree)

The Ranking: Real-time model evaluation

Modelsn=0n=1n=2n=3n=4avg
Bag-D30.7280.9320.9720.9980.9940.925
CSR-D20.7070.9380.97611.0040.925
BBoost-D1F0.7250.9250.9781.0061.0010.927
Bag-D2F0.7720.910.9670.9940.9990.929
Ridge-D10.7640.9240.9580.9961.0010.929
Ridge-D20.7420.9260.9721.0011.0050.929
Bag-D20.7670.9210.9690.9931.0020.93
T.ARDI(2)0.7450.9290.9751.0021.0070.932
Bag-D1F0.7590.9250.971.0051.0010.932
Bag-D10.7830.9180.9610.9961.0020.932
CSR-D10.7580.9340.9741.0041.0090.936
Ridge-D30.7910.9210.9691.0041.0030.938
RF-D1F0.8160.9170.9660.9950.9920.938
Ridge-D2F0.8080.9130.9680.9991.0040.938
CBoost-D1F0.7730.9250.9831.011.0010.938
SVR-D10.8250.9190.9640.9920.9950.939
LASSO-D1F0.7870.930.9781.0020.9990.939
AdaLASSO-D1F0.7810.9370.981.0030.9980.94
RF-D10.8040.9240.9710.9991.0030.94
Bag-D3F0.8160.9170.9721.0050.9930.941
EN-D2F0.8160.9210.9690.9991.0010.941
EN-D1F0.7990.9280.9781.00110.941
SVR-D30.8270.920.970.9950.9970.942
RF-D30.8120.9360.9690.9941.0010.942
ARDI(1)0.7990.9390.9750.9991.0010.943
CBoost-D2F0.8210.920.9650.9991.0080.943
BBoost-D2F0.8360.9120.9670.99810.943
SVR-D3F0.8310.9140.9720.9951.0010.943
SVR-D20.8280.9230.9710.9970.9970.943
AdaEN-D20.7660.9470.9771.0031.0240.943
EN-D20.7770.9710.9780.9960.9940.943
RF-D20.8160.9250.9761.00110.943
LASSO-D2F0.8310.920.9690.99810.944
AdaEN-D1F0.8020.9340.9811.00210.944
SVR-D1F0.8270.9210.9710.99910.944
CSR-D2F0.8240.9280.970.99710.944
SVR-D2F0.8310.9250.9720.9960.9950.944
RF-D2F0.8270.9290.9720.9980.9950.944
BTree-D1F0.8110.9230.9751.0021.0080.944
AdaEN-D2F0.8340.9260.9650.9991.0020.945
AdaLASSO-D2F0.840.9220.9660.9981.0010.945
AdaLASSO-D20.7650.9540.9790.9981.0340.946
LSTM-D1F0.8450.9180.9681.0050.9940.946
ARDI(2)0.7980.9470.9781.0011.0080.946
EN-D30.8340.9240.975110.947
CSR-D30.8140.9310.981.0051.0030.947
RW0.8390.9210.9750.9990.9990.947
LSTM-D2F0.8440.9280.96610.9970.947
LASSO-D20.780.980.98310.9970.948
BVAR-CSV0.8110.9260.981.0121.0130.948
RF-D3F0.8440.9340.9760.9911.0010.949
T.ARDI(1)0.8140.9480.9751.0051.0070.95
AdaEN-D10.8170.9310.9911.0071.0030.95
BBoost-D3F0.8650.9060.9731.0061.0010.95
AdaEN-D30.8480.9280.97611.0020.951
LSTM-D3F0.8510.9260.9720.9941.0130.951
SgLASSO-D30.8490.9250.9781.0031.0050.952
SgLASSO-D3F0.8610.9120.9771.0111.0030.953
CSR-D1F0.8770.9250.970.9980.9990.954
EN-D10.8090.9360.9441.0451.0360.954
BTree-D10.8120.9330.9881.0151.0240.955
AdaEN-D3F0.8830.9240.9681.0010.9990.955
LSTM-D30.8580.9320.9781.0051.0040.955
BVAR-Minn0.850.9330.9741.0121.0150.957
EN-D3F0.8910.9260.97110.9990.957
CBoost-D3F0.8540.9360.9851.0131.0010.958
LSTM-D10.8660.9360.9781.0021.0130.959
CSR-D3F0.890.9380.971.0010.9970.959
LASSO-D3F0.9030.9210.97111.0030.96
AdaLASSO-D3F0.8930.9290.9711.0031.0040.96
BTree-D2F0.850.9540.9911.0170.9970.962
LSTM-D20.8680.9430.9861.0170.9990.962
BTree-D20.8390.9520.9941.0121.0160.963
Ridge-D3F0.8020.9411.0171.0461.0330.968
AdaLASSO-D10.830.9471.0191.0371.0220.971
BTree-D30.8510.9880.991.0161.0210.973
Ridge-D1F0.7790.9591.0261.061.0540.976
LASSO-D30.8320.9321.0181.061.0560.98
AdaLASSO-D30.8340.9451.031.0651.060.987
LASSO-D10.7741.0070.9871.1021.0740.989
BTree-D3F0.881.0310.9991.011.0330.991
AR(BIC)1.0081.00311.00111.002
AR(CV)1.021.00411.0010.9991.005
AR(4)1.0491.0171.0020.9990.9991.013
AR(1)NaNNaNNaNNaNNaNNaN

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diff --git a/sota/index.html b/sota/index.html
index 18f534e2..d5c1452a 100644
--- a/sota/index.html
+++ b/sota/index.html
@@ -3,7 +3,7 @@
 Macro Nowcasting | Haris Karagiannakis
Macro Nowcasting
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.
The 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.
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: CPIAUCSL).
  • SOTA Nowcasts
  • MSc Nowcasts
References
  • Babii, A., Ghysels E., & Striaukas, J. (2022). “Machine learning time series regressions with an application to nowcasting.” Journal of Business & Economic Statistics, 40(3), 1094-1106.
  • Stock, J. H., & Watson, M. W. (2002). “Macroeconomic forecasting using diffusion indexes.” Journal of Business & Economic Statistics, 20(2), 147-162.

+Automatic

Macro Nowcasting

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.

The 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.

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: CPIAUCSL).

  • SOTA Nowcasts

  • MSc Nowcasts

References

  • Babii, A., Ghysels E., & Striaukas, J. (2022). “Machine learning time series regressions with an application to nowcasting.” Journal of Business & Economic Statistics, 40(3), 1094-1106.
  • Stock, J. H., & Watson, M. W. (2002). “Macroeconomic forecasting using diffusion indexes.” Journal of Business & Economic Statistics, 20(2), 147-162.