THE GOVERNMENT OF THE REPUBLIC OF SLOVENIA INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT Iasi, 26 SEPTEMBER 2008 Forecasting macroeconomic variables with dynamic factor models – The case of Slovenia Marko Glažar

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT Outline Introduction Theoretical background Data Results –Pseudo out-of-sample analysis –Past forecasts compared to realization Conclusion

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT Theoretical background 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:

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT Theoretical background The disturbances are unobserved and it holds: s.t. For the strict factor model it holds: A dynamic factor model can be estimated by principal components For a known number of factors we have a nonlinear least square problem:

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT Theoretical background 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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 Theoretical background 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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,…

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT Forecasting performance for annual GDP growth Forecasts for the year 2007 Forecasts for the year 2008

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT Forecasts with DFM for the year 2007 compared to the realization and IMAD official forecasts Forecasting performance of the growth of GDP components

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT 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

INSTITUTE OF MACROECONOMIC ANALYSIS AND DEVELOPMENT Reference: IMAD Working Paper Series Brezigar Masten A., Glažar M., Kušar J., Masten I.: Forecasting Macroeconomic Variables in Slovenia Using Dynamic Factor Models Contact: