Probabilistic predictive analysis of business cycle fluctuations in Polish economy

Błażej Mazur

doi:10.24136/eq.v12i3.23

Abstract

Research background: The probabilistic setup and focus on evaluation of uncertainties and risks has become more widespread in modern empirical macroeconomics, including the analysis of business cycle fluctuations. Therefore, forecast-based indicators of future economic conditions should be constructed using density forecasts rather than point forecasts, as the former provide description of forecast uncertainty. Purpose of the article: We discuss model-based probabilistic inference on business cycle fluctuations in Poland. In particular, we consider model comparison for probabilistic prediction of growth rates of the Polish industrial production. We also develop a class of indicators of future economic conditions constructed using probabilistic information on the rates (that make use of joint predictive distribution over several forecast horizons). Methods: We use Bayesian methods (in order to capture the estimation uncertainty) and consider two groups of models. The first group consists of Dynamic Conditional Score models with the generalized t conditional distribution (with conditional heteroskedasticity and heavy tails, being important for modelling of extreme observations). Another group of models relies on deterministic cycle modelling using Flexible Fourier Form. Ex-post density forecasting performance of the models is compared using the criteria for probabilistic pre-diction: Log-Predictive Score (LPS) and Continuous Ranked Probability Score (CRPS). Findings & value added: The pre-2013 data support the deterministic cycle models whereas more recent observations can be explained by a simple mean-reverting Gaussian AR(4) process. The results indicate a structural change affecting Polish business cycle fluctuations after 2013. Hence, forecast pooling strategies are recommended as a tool for further research. We find rather limited support in favor of the first group of models. The probabilistic indicator of future economic conditions considered here leads actual phases of the growth cycle quite well, though the effect is less obvious after 2013.

Highlights

The purpose of the paper is to set up a methodology that allows for practical predictive business cycle analysis based on industrial production data
We focus on fine-tuning of a univariate specification in terms of more subtle properties like the form of the conditional distribution and dynamic evolution of conditional mean and variance — this is because long-term predictive properties are of interest here
Specifications of the first group capture business cycle dynamics using the deterministic cycle approach based on Flexible Fourier Form

Summary

Introduction

The purpose of the paper is to set up a methodology that allows for practical predictive business cycle analysis based on industrial production data. We assume that inference about future evolution of business cycle conditions should be model-based and take into account the estimation and the prediction uncertainty. A model that is used to generate forecasts underlying any analysis of future business conditions should display satisfactory performance in terms of point forecasts, and density forecasts. The density forecast is constructed as a joint (potentially multivariate over horizons) distribution which provides a formal description of uncertainty as to future values of the analyzed variable. We make use of the forecasts to construct a probabilistic indicator describing future prospects as to the growth rates of the industrial production index. The concept of indicator is not necessarily the most widely used one, given, for example, an alternative formulation by Barhoumi et al (2016)

Objectives

Conclusion