Density Forecasts of Polish Industrial Production: a Probabilistic Perspective on Business Cycle Fluctuations
Current approaches used in empirical macroeconomic analyses use the probabilistic setup and focus on evaluation of uncertainties and risks, also with respect to future business cycle fluctuations. Therefore, forecast-based business conditions indicators should be constructed using not just point forecasts, but rather density forecasts. The latter represent whole predictive distribution and provide relevant description of forecast uncertainty.We discuss a problem of model-based probabilistic inference on business cycle conditions in Poland. In particular we consider a model choice problem for density forecasts of Polish monthly industrial production index and its selected sub-indices. Based on the results we develop indicators of future economic conditions constructed using probabilistic information on future values of the index. In order to develop a relevant model class we make use of univariate Dynamic Conditional Score models with Bayesian inference methods. We assume that the conditional distribution is of the generalized t form in order to allow for heavy tails. Another group of models under consideration relies on the idea of business cycle modelling using the Flexible Fourier Form. We compare performance of alternative models based on ex-post evaluation of density forecasting accuracy using such criteria as Log-Predictive Score (LPS) and Continuous Ranked Probability Score (CRPS). The assessment of density forecasting performance for Polish industrial production index turns out to be difficult since it depends on the choice of verification window. The pre-2013 data supports the deterministic cycle model whereas more recent observations can be explained by a very simple mean-reverting Gaussian AR(4) process. This provides an indirect evidence indicating the change of pattern of Polish business cycle fluctuations after 2013. A probabilistic indicator of business conditions is also sensitive to details of its construction. The results suggest application of forecast pooling strategies as a goal for further research.
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