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Forecasting Performance of Information Criteria with Many Macro Series

Author

Listed:
  • Clive Granger
  • Yongil Jeon

Abstract

Stock & Watson (1999) consider the relative quality of different univariate forecasting techniques. This paper extends their study on forecasting practice, comparing the forecasting performance of two popular model selection procedures, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). This paper considers several topics: how AIC and BIC choose lags in autoregressive models on actual series, how models so selected forecast relative to an AR(4) model, the effect of using a maximum lag on model selection, and the forecasting performance of combining AR(4), AIC, and BIC models with an equal weight.

Suggested Citation

  • Clive Granger & Yongil Jeon, 2004. "Forecasting Performance of Information Criteria with Many Macro Series," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(10), pages 1227-1240.
  • Handle: RePEc:taf:japsta:v:31:y:2004:i:10:p:1227-1240
    DOI: 10.1080/0266476042000285495
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    Citations

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    Cited by:

    1. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España;Working Papers Homepage.
    2. Carlos A. Medel, 2015. "Probabilidad Clásica de Sobreajuste con Criterios de Información: Estimaciones con Series Macroeconómicas Chilenas," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 30(1), pages 57-72, Abril.
    3. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
    4. Medel, Carlos A. & Salgado, Sergio C., 2012. "Does BIC Estimate and Forecast Better than AIC?," MPRA Paper 42235, University Library of Munich, Germany.
    5. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
    6. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo, vol. 56(2), pages 192-220, June.
    7. repec:lrk:eeaart:35_2_5 is not listed on IDEAS
    8. McLeod, A. Ian & Zhang, Ying, 2008. "Improved Subset Autoregression: With R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i02).
    9. Pablo M. Pincheira & Carlos A. Medel, 2015. "Forecasting Inflation with a Simple and Accurate Benchmark: The Case of the US and a Set of Inflation Targeting Countries," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(1), pages 2-29, January.
    10. Nezir Kose & Nuri Ucar, 2006. "Effect of cross correlations in error terms on the model selection criteria for the stationary VAR process," Applied Economics Letters, Taylor & Francis Journals, vol. 13(4), pages 223-228.
    11. Elif Cepni & Nezir Kose, 2006. "Assessing the Currency Crises in Turkey," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 6(1), pages 37-64.

    More about this item

    Keywords

    Large macro model; information criterion; AIC; BIC;

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