Density Forecasts Based on Disaggregate Data: Nowcasting Polish Inflation
DOI:
https://doi.org/10.12775/DEM.2015.004Keywords
prediction, model comparison, density forecasting, inflation, VAR models, shrinkageAbstract
The paper investigates gains in performance of density forecasts from models using disaggregate data when forecasting aggregate series. The problem is considered within a restricted VAR framework with alternative sets of exclusion restrictions. Empirical analysis of Polish CPI m-o-m inflation rate (using its 14 sub-categories for disaggregate modelling) is presented. Exclusion restrictions are shown to improve density forecasting performance (as evaluated using log-score and CRPS criteria) relatively to aggregate and also disaggregate unrestricted models.References
Aron, J., Muellbauer, J. (2013), New Methods for Forecasting Inflation, Applied to the US*, Oxford Bulletin of Economics and Statistics 75 (5), 637–661, DOI: http://dx.doi.org/10.1111/j.1468-0084.2012.00728.x.
Castle, J. L., Hendry, D. F. (2010), Nowcasting From Disaggregates in the Face of Location Shifts, Journal of Forecasting, 29(1-2), 200–214, DOI: http://dx.doi.org/10.1002/for.1140.
Clark, T. (2006), Disaggregate Evidence on the Persistence of Consumer Price Inflation, Journal of Applied Econometrics, 21(5), 563–587, DOI: http://dx.doi.org/10.1002/jae.859.
Clark, T., Ravazzolo, F. (2015), Macroeconomic Forecasting Performance under Alternative Specifications of Time-Varying Volatility, Journal of Applied Econometrics, 30(4), 551–575, DOI: http://dx.doi.org/10.1002/jae.2379.
Dees, S., Guntner, J. (2014), Analysing and Forecasting Price Dynamics Across Euro Area Countries and Sectors: A Panel VAR Approach, Economics working papers 2014-10, Department of Economics, Johannes Kepler University Linz, Austria.
Doornik, J. A.., Ooms, M. (2003), Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models, Computational Statistics & Data Analysis, 42(3), 333–348, DOI: http://dx.doi.org/10.1016/S0167-9473(02)00212-8.
Faust, J., Wright, J.H. (2013), Forecasting Inflation, in Elliott G., Timmermann. A. (eds.), Handbook of Economic Forecasting, vol 2A, Amsterdam, North Holland, DOI: http://dx.doi.org/10.1016/B978-0-444-53683-9.00001-3.
Frühwirth-Schnatter S., Wagner. H. (2010), Stochastic model specification search for Gaussian and partial non-Gaussian state space models, Journal of Econometrics, 154, 85–100, DOI: http://dx.doi.org/10.1016/j.jeconom.2009.07.003.
George, E. I., Sun, D., Ni. S. (2008), Bayesian Stochastic Search for VAR Model Restrictions, Journal of Econometrics, 142(1), 553–580, DOI: http://dx.doi.org/10.1016/j.jeconom.2007.08.017.
Giacomini, R., Granger, C. (2004), Aggregation of Space-Time Processes, Journal of Econ-ometrics, 118(1-2), 7–26, DOI: http://dx.doi.org/10.1016/S0304-4076(03)00132-5.
Gneiting, T., Raftery, A. (2007), Strictly Proper Scoring Rules, Prediction, and Estimation, Journal of the American Statistical Association, 102(477), 359–378, DOI: http://dx.doi.org/10.1198/016214506000001437.
Hendry, D.F., Hubrich, K. (2011), Combining Disaggregate Forecasts or Combining Dis-aggregate Information to Forecast an Aggregate, Journal of Business & Economic Sta-tistics, 29(2), 216–227, DOI: http://dx.doi.org/10.1198/jbes.2009.07112.
Hubrich, K.. (2005), Forecasting Euro Area Inflation: Does Aggregating Forecasts by HICP Component Improve Forecast Accuracy?, International Journal of Forecasting, 21(1), 119–136, DOI: http://dx.doi.org/10.1016/j.ijforecast.2004.04.005.
Huwiler, M., Kaufmann, D. (2013), Combining Disaggregate Forecasts for Inflation: The SNB's ARIMA model, Economic Studies 2013-07, Swiss National Bank.
Ibarra, R. (2012), Do Disaggregated CPI Data Improve the Accuracy of Inflation Forecasts?, Economic Modelling, 29(4), 1305–1313, DOI: http://dx.doi.org/10.1016/j.econmod.2012.04.017.
Lütkepohl, H. (2009), Forecasting Aggregated Time Series Variables: A Survey, Economics Working Papers ECO2009/17, European University Institute.
Stock, J. H., Watson, M. (2015), Core Inflation and Trend Inflation, NBER Working Paper No. 21282.
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