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¿Akaike o Schwarz? ¿Cuál elegir para Predecir el PIB Chileno?

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  • Carlos Medel

Abstract

Schwarz. In this paper I evaluate the predictive ability of the Akaike and Schwarz information criteria using autoregressive integrated moving average models, with sectoral data of Chilean GDP. In terms of root mean square error, and after the estimation of more than a million models, the results indicate that —on average— the models based on the Schwarz criterion perform better than those selected with the Akaike, for the four horizons analyzed. Furthermore, the statistical significance of these differences indicates that the superiority in favor of the Schwarz criterion holds mainly at higher horizons.

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Bibliographic Info

Paper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 658.

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Date of creation: Jan 2012
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Handle: RePEc:chb:bcchwp:658

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  1. Nishii, R., 1988. "Maximum likelihood principle and model selection when the true model is unspecified," Journal of Multivariate Analysis, Elsevier, vol. 27(2), pages 392-403, November.
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  4. Guy Melard & Jean-Michel Pasteels, 2000. "Automatic ARIMA modeling including interventions, using time series expert software," ULB Institutional Repository 2013/13744, ULB -- Universite Libre de Bruxelles.
  5. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
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  7. Sawa, Takamitsu, 1978. "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, Econometric Society, vol. 46(6), pages 1273-91, November.
  8. Carlos Medel, 2012. "How Informative are In–Sample Information Criteria to Forecasting? The Case of Chilean GDP," Working Papers Central Bank of Chile, Central Bank of Chile 657, Central Bank of Chile.
  9. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, Econometric Society, vol. 48(1), pages 1-48, January.
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  14. Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile, Central Bank of Chile 545, Central Bank of Chile.
  15. Kilian, Lutz, 2001. "Impulse Response Analysis in Vector Autoregressions with Unknown Lag Order," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(3), pages 161-79, April.
  16. Poskitt, D.S., 1994. "A Note on Autoregressive Modeling," Econometric Theory, Cambridge University Press, vol. 10(05), pages 884-899, December.
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  18. Pablo Pincheira Brown & Álvaro García Marín, 2009. "Forecasting Inflation in Chile With an Accurate Benchmark," Working Papers Central Bank of Chile, Central Bank of Chile 514, Central Bank of Chile.
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Cited by:
  1. Carlos J. García & Pablo González M. & Antonio Moncado S., 2013. "Macroeconomic Forecasting in Chile: a Structural Bayesian Approach," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 16(1), pages 24-63, April.
  2. Stephanie Schmitt-Grohé & Martín Uribe, 2012. "Pegs, Downward Wage Rigidity, and Unemployment: the Role of Financial Structure," Working Papers Central Bank of Chile, Central Bank of Chile 672, Central Bank of Chile.
  3. Carlos Garcia, 2012. "Impacto del Costo de la Energía Eléctrica en la Economía Chilena: Una Perspectiva Macroeconómica," ILADES-Georgetown University Working Papers, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines inv281, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines.

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