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Forecasting macroeconomic variables with dynamic factor models – The case of Slovenia

Iasi , 26 SEPTEMBER 2008. Forecasting macroeconomic variables with dynamic factor models – The case of Slovenia. Marko Glažar. Outline. Introduction Theoretical background Data Results Pseudo out-of-sample analysis Past forecasts compared to realization Conclusion. Introduction.

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Forecasting macroeconomic variables with dynamic factor models – The case of Slovenia

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  1. Iasi, 26 SEPTEMBER 2008 Forecastingmacroeconomicvariableswithdynamicfactormodels – ThecaseofSlovenia Marko Glažar

  2. Outline • Introduction • Theoretical background • Data • Results • Pseudo out-of-sample analysis • Past forecasts compared to realization • Conclusion

  3. Introduction • Dynamic factor models (DFM) • Used for forecasting, business cycle investigation, monetary policy • IMAD uses DFM for forecasting growth of GDP and components • The forecasts are not official, used as a support for experts’ forecast • The DFM approach was developed for IMAD by Igor Masten, University of Ljubljana, Faculty of Economics • The model is more thoroughly described in IMAD working paper

  4. Theoreticalbackground • N series, vector in time t • Each element can be represented as: • vector lag polynomial – dynamic factor loading • vector of r common factors • idiosyncratic disturbance • if is of a finite order q =1, then • where Dynamic r – factor model:

  5. Theoreticalbackground • The disturbances are unobserved and it holds: • Forthestrictfactor model it holds: • A dynamicfactor model canbeestimatedby principal components • For a knownnumberoffactorswehave a nonlinearleastsquare problem: s.t.

  6. Theoreticalbackground • Approximate dynamic model: • Allowed weak serial correlation of the idiosyncratic errors • Idiosyncratic errors may be cross-correlated and heteroscedastic • Allowed weak correlation among factors and idiosyncratic components • Forecasting models: h – forecast horizon

  7. Theoreticalbackground • Altogether we have 158 different models. Differentiated by: • Number of factors, unbalanced or balanced panel • Inclusion of the AR component • Inclusion of the factor lags • Inclusion of the intercept correction • Relative mean squared error is the measure for comparison of the models • MSE of the factor models is compared to the MSE of the AR model in the pseudo out-of-sample analysis

  8. Data • Dataset consists of 80 quarterly series, from 1994: • National account data • Survey data – confidence indicators • Prices • Foreign trade • Production indices • Labour market • Financial variables • Sources: Eurostat, Statistical Office of the Republic of Slovenia, Centre for European Economic Research, Bank of Slovenia, Ministry of Finance,…

  9. In sample forecasting performance • In sample forecasts for GDP growth one horizon ahead, performance of the best factor model (relative MSE = 0.48) and AR model

  10. In sample forecasting performance • In sample forecasts for GDP growth one horizon ahead, performance of the best factor model (relative MSE = 0.48) and AR model

  11. In sample forecasting performance • In sample forecasts for GDP growth one horizon ahead, performance of the best factor model (relative MSE = 0.48) and AR model

  12. In sample forecasting performance • In sample forecasts for GDP growth 4 horizons ahead, performance of the best factor model (relative MSE = 0.29) and AR model

  13. In sample forecasting performance • In sample forecasts for GDP growth 4 horizons ahead, performance of the best factor model (relative MSE = 0.29) and AR model

  14. In sample forecasting performance • In sample forecasts for GDP growth 4 horizons ahead, performance of the best factor model (relative MSE = 0.29) and AR model

  15. In sample forecasting performance • In sample forecasts for INDUSTRIAL PRODUCTION growth one horizon ahead, performance of the best factor model (relative MSE = 0.69) and AR model

  16. In sample forecasting performance • In sample forecasts for INDUSTRIAL PRODUCTION growth one horizon ahead, performance of the best factor model (relative MSE = 0.69) and AR model

  17. In sample forecasting performance • In sample forecasts for INDUSTRIAL PRODUCTION growth one horizon ahead, performance of the best factor model (relative MSE = 0.69) and AR model

  18. Forecasting performance for annual GDP growth • Forecasts for the year 2007 • Forecasts for the year 2008

  19. Forecasting performance ofthegrowthof GDP components • Forecasts with DFM for the year 2007 compared to the realization and IMAD official forecasts

  20. Concluding remarks • With a good dataset DFM perform better than simple AR models • We use additional improvements such as preselection of the variables and use of lagged series in extracting the factors • Problem with the revisons of the data (by Statistical office) • We use the DFM also for forecasting inflation, using disaggregated data on CPI components

  21. Reference: IMAD Working Paper Series http://www.umar.gov.si/en/publications/working_papers Brezigar Masten A., Glažar M., Kušar J., Masten I.: Forecasting Macroeconomic Variables in Slovenia Using Dynamic Factor Models Contact: marko.glazar@gov.si

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