Unobserved Component Model for Forecasting Polish Inflation

Kwiatkowski, Jacek

doi:10.12775/DEM.2010.010

Author

Kwiatkowski Jacek (Nicolaus Copernicus University in Toruń, Poland)

Title

Unobserved Component Model for Forecasting Polish Inflation
Prognozowanie inflacji w Polsce przy użyciu modelu lokalnego poziomu

Source

Dynamic Econometric Models, 2010, vol. 10, s. 121-129, rys., tab., bibliogr. 14 poz.

Keyword

Prognozowanie, Inflacja, Modele ekonometryczne
Forecasting, Inflation, Econometric models

Note

summ., streszcz.

Abstract

W artykule przeprowadzono badania dotyczące trafności prognoz otrzymanych za pomocą modelu lokalnego poziomu w wersji Stocka i Watsona (2008). Rozważono różne postacie tego modelu i zbadano, które z nich dają możliwość uzyskania najtrafniejszej prognozy. Badania empiryczne dotyczyły inflacji w Polsce w latach 1992-2008. Ostatni rok posłużył do oceny jakości prognoz. Badania przeprowadzono na podstawie wskaźnika cen konsumenta CPI. Uzyskane wyniki nie potwierdzają jednoznacznej przewagi modelu lokalnego poziomu, w prognozowaniu inflacji, nad standardowym modelem autoregresyjnym. Wszystkie modele uzyskały zadowalającą dokładność prognozy. (abstrakt oryginalny)

This paper aims to use the local level models with GARCH and SV errors to predict Polish inflation. The series to be forecast, measured monthly, is consumer price index (CPI) in Poland during 1992-2008. We selected three forecasting models i.e. LL-GARCH(1,1) with Normal or Student errors and LL-SV. A simple AR(2)-SV model is used as a benchmark to assess the accuracy of prediction. The presented results indicate, that there is no clear advantage of LL models in forecasting Polish inflation over standard AR(2)-SV model, although all the models give satisfactory results. (original abstract)

Accessibility

The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of Poznań University of Economics and Business

Full text

Bibliography

Bos, C. (2001), Time Varying Parameter Models for Inflation and Exchange Rates, WebDOC.
Diebold, F.X., Mariano, R.S. (1995), Comparing Predictive Accuracy, Journal of Business and Economic Statistics, 13, 253-63.
Durbin, J., Koopman, S.J. (2001), Time Series Analysis by State Space Methods, Oxford University Press, Oxford.
Fallenbuchl, Z.M. (1994), Foreign Trade in the Process of Transformation in Poland, Atlantic Economic Journal, 22, 2, 51-60.
Grassi, S., Proietti, T. (2008), Has the Volatility of U.S. Inflation Changed and How?, MPRA Paper 11453.
Harvey, A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press, Cambridge.
Koop, G. (2003), Bayesian Econometrics, John Wiley & Sons.
Koop, G., Potter, S. (2001), Are Apparent Findings of Nonlinearity Due to Structural Instability in Economic Time Series?, The Econometrics Journal, 4, 1, 37-55.
Muth, J.F. (1960), Optimal Properties of Exponentially Weighted Forecasts, Journal of the American Statistical Association, 55, 299-306.
Pellegrini, S., Ruiz, E., Espasa, A. (2007), The Relationship Between ARIMA-GARCH and Unobserved Component Models with GARCH Disturbances, Working Paper, ws072706.
Pellegrini, S., Ruiz, E., Espasa, A. (2008), ARIMA-GARCH and Unobserved Component Models with GARCH Disturbances: Are their Prediction Intervals Different? Job Market Paper.
Stock, J.H., Watson, M.W. (2007), Why has U.S. Inflation Become Harder to Forecast? Journal of Money, Credit, and Banking, 39, 3-33.
Stock, J.H., Watson, M.W. (2008), Phillips Curve Inflation Forecasts, Working Paper, 14322.
West, M., Harrison, J. (1989), Bayesian Forecasting and Dynamic Models, Springer.

Cited by

ISSN

1234-3862

Language

eng

URI / DOI

BazEkon - The Main Library of the Cracow University of Economics

Main menu