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Nowcasting Israel GDP

Nowcasting Israel GDP. Gil Dafnai , Jonathan Sidi. Research Department, Bank of Israel. Motivation : GDP data is being published at a six week lag after the end of the relevant quarter (and it is needed sooner).

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Nowcasting Israel GDP

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  1. Nowcasting Israel GDP Gil Dafnai , Jonathan Sidi Research Department, Bank of Israel

  2. Motivation : GDP data is being published at a six week lag after the end of the relevant quarter (and it is needed sooner). However : There is a lot of monthly data that is available before the policy meetings. Therefore : We use real-time monthly data in order to Nowcast the GDP 3 weeks ahead of publication.

  3. Comparing Nowcasting Methods

  4. General Data Selection • General Data Set ( ): • 170 monthly Indicators: 95% Domestic and 5% Global. • History: • All series begin at least at 1998Q1. • Endpoints: • All series have value for at least two month of the projected quarter.

  5. Seasonal adjustment by X12-ARIMA Holt and Winters exponential smoother is applied where necessary Convert to lower frequency (quarterly) by average observation Convert to percent change Standardize The resulting sample size is defined as

  6. Unconditional Selection Methods Conditional Selection Methods Sparse PCA PCA Multiple Univariate LASSO Elastic Net • Two Component Norm • Iterated Component • Selected Loadings Stepwise Regression Stepwise Regression Intermediary Step Final Step

  7. Multiple Univariate Regression Benchmark Method

  8. Unconditional Selection Methods Conditional Selection Methods Multiple Univariate LASSO Elastic Net Stepwise Regression Stepwise Regression Intermediary Step Final Step

  9. Univariate Regression • RUN: • Calculate AICi - and keep the top 25 (in Z) • Run Stepwise Backward regression on: • Calculate Static Forecast

  10. Unconditional Methods PCA and SPCA

  11. Unconditional Selection Methods Conditional Selection Methods Sparse PCA PCA Multiple Univariate LASSO Elastic Net • Two Component Norm • Iterated Component • Selected Loadings Stepwise Regression Stepwise Regression Intermediary Step Final Step

  12. Principal Component Analysis Original Data Centered Data Rotated Centered Data Projection on max variance axis SC1

  13. Biplot of PC1 vs. PC2 Too many variables causes the inference to be extremely difficult What characteristic do PC1 or PC2 represent in the data???

  14. Biplot of SPC1 vs. SPC2 Same Data Set!!! Retail Sales Indices IL and US Consumer Confidence Purchasing Manager’s Indices

  15. Primary deficiency in SPCA The amount of variance in the data that is explained by the PCs decreases as sparsity increases

  16. Sparse Principal Component Analysisregression approach to PCA L2-norm L1-norm Is the ith row of the data matrix

  17. Application in Regression Three Methods Loadings Matrix The classic approach (dimension reduction) Two component norm (TCN) (variable selection) Iterated component (IC) (Jolliffe 1973) (variable selection)

  18. Conditional Methods LASSO and Elastic Net

  19. Unconditional Selection Methods Conditional Selection Methods Multiple Univariate LASSO Elastic Net Stepwise Regression Stepwise Regression Intermediary Step Final Step

  20. Lasso VS. Ridge

  21. Example Using a SubsampleRidge

  22. Example Using a SubsampleLASSO

  23. LASSO Conclusions • Advantages • General form of algorithm makes it applicable to many problems in econometrics. • Ability to produce decomposition of variable contribution of the forecast. • Shortcomings • Can not select more then n variables • If n>p then ridge is better • No grouping

  24. Unconditional Selection Methods Conditional Selection Methods Multiple Univariate LASSO Elastic Net Stepwise Regression Stepwise Regression Intermediary Step Final Step

  25. Elastic Net LASSO RIDGE L2-norm L1-norm

  26. Example Using a SubsampleElastic Net 25% 50% 75%

  27. Algorithm Behavior Subject to Different Values of Lambda q75 q50 CBS Bias

  28. Algorithm Behavior Subject to Different Values of Alpha

  29. Results

  30. Current Release vs. First Release Seasonally Adjusted, Annual Percent Change First Release will be used as control group

  31. Comparison of Selection MethodsRolling Regression (2004Q1-2010Q2)

  32. Absolute Errors from Current Release

  33. Mean Absolute Errors from Current ReleaseJackknife Procedure

  34. Comparison of Variable Selection Consistency (24 Periods)

  35. Variable Contribution for Nowcast2006Q2-2006Q4 (LASSO)

  36. Deriving the GDP from NA Identities Tomer Kriaf Research Department, Bank of Israel

  37. Y=C+G+I+X-M • Consumption Equation:Import of Durables, VAT, Confidence Index, Revenue Index (L), Imports of Raw Materials, TA Stock Market Index. • Fixed Capital Formation Equation:Imports of investment Goods, Capital Utilization, PMI, lagged Inventories, TA Stock Market Index. • Inventories Equation:Exports of goods, Revenue Index, Industrial Production Index. • Exports Equation:Exports of Goods, PMI-USA. • Import Equation:Imports of Goods, Imports of Services. • GDP Equation:Derived GDP, Indirect Tax, Income Tax, TA Stock Market Index. Derived GDP

  38. GDP Nowcasting Performance In Sample Out of Sample Actual

  39. GDP Real Time Nowcasting Performance CBS Current Release Path Forecast CBS First Release Real Time Nowcast

  40. Thank You • Gil Dafnai and Jonathan Sidi • Research Department • Bank of Israel

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