An Unobserved Components Model to Forecast Austrian GDP
This paper deals with forecasting quarterly Austrian GDP growth using monthly conjunctural indicators and state space models. The latter provide an efficient econometric framework to analyse jointly data with different frequencies. Based on a Kalman filter technique we estimate a monthly GDP growth series as an unobserved component using monthly conjunctural indicators as explanatory variables. From a large data set of more than 150 monthly indicators the following six explanatory variables were selected on the basis of their in-sample fit and out of sample forecast performance: the ifo-index, credit growth, vacancies, the real exchange rate, the number of employees and new car registrations. Subsequently, quarterly GDP figures are derived from the monthly unobserved component using a weighted aggregation scheme. Several tests for forecasting accuracy and forecasting encompassing indicate that the unobserved components model (UOC-model) is able to outperform simple ARIMA and Naïve models.
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