Forecasting industrial production in Poland – a comparison of different methods

• Authors: Jan Acedański
• Languages of publication: EN

Abstracts

EN

In this paper we compared the accuracy of a few forecasting methods of the industrial production index in Poland. Naïve forecasts, simple autoregressive models, leading indicator models, factor models as well as joint models were included in the considerations. We used the out-of-sample RMSE and CPA tests as the main measures of the predictions accuracy. We found that three models provided the best predictions in most cases – the models with the PMI index and with the PMI and German IFO indexes as leading indicators as well as joint forecasts.

Keywords

EN

forecasting; industrial production; leading indicators; factor models

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Econometrics. Ekonometria. Advances in Applied Data Analytics
• Year: 2013
• Issue: 1(39)
• Pages: 40-51

Dates

published

2013

Contributors

author

Jan Acedański

• Uniwersytet Ekonomiczny w Katowicach

References

• Acedański J. (2012), Specyfikacja a własności prognostyczne modeli czynnikowych. Paper presented on the 6th National Scientific Conference Modeling and Forecasting Socio-Economic Phenomena, Zakopane 15–18.05.2012.
• Bai J., Ng S. (2008), Forecasting economic time series using targeted predictors, Journal of Econometrics 146: 304–317.
• Baranowski P., Leszczyńska A., Szafrański G. (2010), Krótkookresowe prognozowanie inflacji z użyciem modeli czynnikowych, Bank i Kredyt 41(4): 28–44.
• Bodo G., Signorini L. (1987), Short-term forecasting of the industrial production index, International Journal of Forecasting 3: 245–259.
• Boivin J., Ng S. (2006), Are more data always better for factor analysis? Journal of Econometrics 132 (1): 169–194.
• Bruno G., Lupi C. (2003), Forecasting euro-area industrial production using (mostly) business surveys data, ISAE Working Papers 33.
• Bruno G., Lupi C. (2004), Forecasting industrial production and the early detection of turning points, Empirical Economics 29 (3): 647–671.
• Bulligan G., Golinelli R., Parigi G. (2010), Forecasting monthly industrial production in real-time: From single equations to factor-based models, Empirical Economics 39 (2): 303–336.
• Eickmeier S., Ziegler C. (2006), How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach, Discussion Paper Series 1: Economic Studies No 42, Deutsche Bundesbank.
• Estrella A., Mishkin F. (1998), Predicting U.S. recessions: Financial variables as leading indicators, The Review of Economics and Statistics 80 (1): 45–61.
• Giacomini R., White H. (2006), Tests of conditional predictive ability, Econometrica 74 (6): 1545–1578.
• Maher J. (1957), Forecasting industrial production, Journal of Political Economy 65: 158–165.
• McGuckin R., Ozyildirim A., Zarnowitz V. (2001), The Composite Index of Leading Economic Indicators: How To Make It More Timely, Economics Program Working Paper Series #2000-01, The Conference Board.
• Parigi G., Golinelli R. (2007), The use of monthly indicators to forecasts quarterly GDP in the short run: An application to the G7 countries, Journal of Forecasting 26 (2): 77–94.
• Siliverstovs B., van Dijk D. (2003), Forecasting industrial production with linear, nonlinear, and structural change models, Econometric Institute Report EI 2003-16, Erasmus University, Rotterdam.
• Stekler H. (1961), Forecasting industrial production, Journal of the American Statistical Association 56 (296): 869–877.
• Stock J., Watson M. (1998), Diffusion Indexes, NBER Working Papers 6702.
• Zizza R. (2002), Forecasting the industrial production index for the euro area through forecasts for the main countries, Economics Working Papers 441, Bank of Italy.