How useful are time-varying parameter models for forecasting economic growth in CESEE?

Niko Hauzenberger, Martin Feldkircher

Research output: Contribution to journalArticlepeer-review


Empirical evidence has shown that a prerequisite for generating reliable macroeconomic forecasts is either the inclusion of a large information set or modeling time variation in the models' parameters and volatilities. In this paper we examine these claims in a comparative manner, forecasting GDP growth for six CESEE economies. We use Bayesian techniques and evaluate the models based on both the accuracy of their point forecasts as well as the degree of uncertainty surrounding these predictions. Our results indicate that forecasts from a fully-fledged time-varying parameter model tend to outperform those from its constant parameter competitors. Adding more information, e.g. from other countries, by contrast, does not improve forecast performance significantly for most of the countries under study. Last, we analyze whether it pays to forecast GDP growth indirectly by summing up forecasts of GDP components. This approach yields competitive forecasts, yet it preserves an economic interpretation of the underlying drivers for the economic growth forecasts, which is of crucial importance from a practitioner’s view.
Original languageEnglish
Pages (from-to)29-48
Number of pages20
JournalFocus on European Economic Integration
Issue numberQ1/19
Publication statusPublished - 1 Apr 2019


  • forecasting
  • time-varying parameter VAR
  • aggregate GDP forecast
  • empirical evidence
  • macroeconomic forecasts


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