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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 heteroscedasticity 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 prediction: 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. (original abstract)
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 heteroscedasticity 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 prediction: 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. (original abstract)
Twórcy
autor
- Cracow University of Economics, Poland
Bibliografia
- Barhoumi K., Darné O., & Ferrara L. (2016). A world trade leading index (WTLI). Economics Letters, 146. doi: 10.1016/j.econlet.2016.07.032.
- Bernardi M., & Catania L. (2016). Comparison of value-at-risk models using the MCS approach. Computational Statistics, 31(2). doi: 10.1007/s00180-016-0646-6.
- Billio M., Casarin R., Ravazzolo F., & Van Dijk H.K. (2016). Interconnections between Eurozone and US booms and busts using a Bayesian panel Markov-Switching VAR model. Journal of Applied Econometrics, 31(7). doi: 10.1002/jae.2501.
- Bjørnland H.C., Ravazzolo F., & Thorsrud L.A. (2017). Forecasting GDP with global components: this time is different. International Journal of Forecasting, 33(1). doi: 10.1016/j.ijforecast.2016.02.004.
- Clark T.E., Ravazzolo F. (2015). Macroeconomic forecasting performance under alternative specifications of time-varying volatility. Journal of Applied Econometrics, 30(4). doi: 10.1002/jae.2379.
- Creal D., Koopman S.J. & Lucas A. (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28. doi: 10.1002/jae.1279.
- Dąbrowski M.A., Śmiech S., & Papież M. (2015). Monetary policy options for mitigating the impact of the global financial crisis on emerging market economies. Journal of International Money and Finance, 51. doi: 10.1016/j.jimonfin.2014.12.006.
- Eo Y., & Kim C.-J. (2016). Markov-switching models with evolving regime specific parameters: are postwar booms or recessions all alike? Review of Economics and Statistics, 98(5). doi: 10.1162/rest_a_00561.
- Ferrara L., Marcellino M., & Mogliani M. (2015). Macroeconomic forecasting during the great recession: the return of non-linearity? International Journal of Forecasting, 31(3). doi: 10.1016/j.ijforecast.2014.11.005.
- Gallant A.R. (1981). On the bias in flexible functional forms and an essentially unbiased form: the Fourier flexible form. Journal of Econometrics, 15. doi: 10.1016/0304-4076(81)90115-9.
- Gneiting T., & Raftery A. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477). doi: 10.1198/016214506000001437.
- Harvey A.C. (2013). Dynamic models for volatility and heavy tails: with applications to financial and economic time series. Cambridge: Cambridge University Press. doi: 10.1017/cbo9781139540933.
- Harvey A.C., & Lange J.-R. (2017). Volatility modelling with a generalized tdistribution. Journal of Time Series Analysis, 38. doi: 10.1111/jtsa.12224.
- Lenart Ł. (2017). Examination of seasonal volatility in HICP for Baltic region countries: non-parametric test versus forecasting experiment. Central European Journal of Economic Modelling and Econometrics, 9(1).
- Lenart Ł., & Mazur B. (2016). On Bayesian Inference for Almost Periodic in Mean Autoregressive Models. Przegląd Statystyczny, 63(3).
- Lenart Ł., & Mazur B. (2017). Business cycle analysis with short time series: a stochastic versus a non-stochastic approach. In: M. Papież and S. Śmiech (Eds.), The 11th Professor Aleksander Zelias international conference on modelling and forecasting of socio-economic phenomena. Conference proceedings. Cracow: Foundation of the Cracow University of Economics. Retrieved form http://pliki.konferencjazakopianska.pl/proceedings_2017/.
- Lenart Ł., Mazur B., & Pipień M. (2016). Statistical analysis of business cycle fluctuations in Poland before and after the crisis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 11(4). doi: 10.12775/equil.2016.035.
- Lenart Ł., & Pipień M. (2017). Non-parametric test for the existence of the common deterministic cycle: the case of the selected European countries. Central European Journal of Economic Modelling and Econometrics, 9(3).
- Lerch S., Thorarinsdottir T.L., Ravazzolo F., & Gneiting T. (2017). Forecaster's dilemma: extreme events and forecast evaluation. Statistical Science, 32(1). doi: 10.1214/16-sts588.
- Nowotarski J., & Weron R. (2017). Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renewable and Sustainable Energy Reviews, doi: 10.1016/j.rser.2017.05.234.
- Theodossiou P. (1998). Financial data and the skewed generalized t distribution. Management Science, 44. doi: 10.1287/mnsc.44.12.1650.
- Theodossiou P., & Savva C.S. (2016). Skewness and the relation between risk and return. Management Science, 62(6). doi: 10.1287/mnsc.2015.2201.
Typ dokumentu
Bibliografia
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