Probabilistic predictive analysis of business cycle fluctuations in Polish economy

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.

Internal factors influencing the cost of equity capital

“…Compared to the external environment represented by macroeconomic and capital market environment (for more details see e.g. Faldzinski et al, 2016;Zinecker et al, 2016;Lenart et al, 2016;Vukovic et al, 2017;Mazur, 2017;Kovacic & Vilotic, 2017;Pietrzak et al, 2017;Meluzin et al, 2017) a company is able to manage the internal factors while influencing the business processes in order to achieve its targets. The capital management is one of the most significant elements of decision-making process.…”

Synchronization of Business Cycles in European Union Countries

“…Dumitru and Dumitru (2010) considers various approaches when modeling business cycles in Romania, namely univariate methods such as the Quadratic trend, the Hodrick-Prescott filter, Band-Pass filter, Beveridge-Nelson decomposition, Wavelet transformation and the principal component analysis. In the case of the Polish economy, Mazur (2017) is modeling the business cycle using a flexible Fourier form.…”

Prediction of business cycle of Poland

“…It is important to note that CLIs offer information about the expected development of the economy, and thus open a debate on the implementation of decisions in the public or private sector (Monni et al 2017;Fabuš, 2017). This is a short-term estimate of the future economic situation, but it can point to important economic changes that are the turning points of the economic cycle (Kramoliš, 2015;Mazur, 2017, Döpke et al 2017). In addition to the prediction itself, CLI can also be used to analyse the performance of economies and thus, to compare their performance within different groups (Saltelli, 2006).…”