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  • Tytuł artykułu:
    Density Forecasts Based on Disaggregate Data: Nowcasting Polish Inflation
  • Opublikowany w czasopiśmie:
  • Rocznik 2015,  tom 15,  numer 0
  • 71−87
  • Oryginalny artykuł naukowy
  • angielski
  • article-1944b810-f0c7-4f9f-a476-1a25f78d628f
  • 8589
  • 10.06.2017 15:02:41
  • Błażej Mazur [1]
  • [1] Cracow University of Economics
  • Brak afiliacji
Nie znaleziono publikacji cytujących ten artykuł
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Huwiler, M., Kaufmann, D. (2013), Combining Disaggregate Forecasts for Inflation: The SNB's ARIMA model, Economic Studies 2013-07, Swiss National Bank.
  15. 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.
  16. Lütkepohl, H. (2009), Forecasting Aggregated Time Series Variables: A Survey, Economics Working Papers ECO2009/17, European University Institute.
  17. Stock, J. H., Watson, M. (2015), Core Inflation and Trend Inflation, NBER Working Paper No. 21282.
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