¿Akaike o Schwarz? ¿Cuál elegir para predecir el PIB chileno?

Medel, Carlos A. (2012): ¿Akaike o Schwarz? ¿Cuál elegir para predecir el PIB chileno?

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 horizo

• Item Type: MPRA Paper
• English Title: Akaike or Schwarz? Which One is a Better Predictor of Chilean GDP?
• Language: Spanish
• Keywords: information criteria; data mining; forecasting; ARIMA
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes

References

1.Akaike, H., 1974, "A New Look at the Statistical Model Identification," IEEE Transactions on Automatic Control 19(6): 716-723.

2.Akaike, H., 1979, "A Bayesian Extension of the Minimum AIC Procedure of Autoregressive Model Fitting," Biometrika 66(2): 237-242.

3.Amemiya, T., 1980, "Selection of Regressors," International Economic Review 21(2): 331-354.

4.Bhansali, R.J., y D.Y. Downham, 1977, "Some Properties of the Order of an Autoregressive Model Selected by a Generalization of Akaike's EPF Criterion," Biometrika 64(3): 547-551.

5.Box, G., y G. Jenkins, 1970, Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, USA.

6.Clark, T.E., 2004, "Can Out-of-Sample Forecast Comparisons Help Prevent Overfitting?," Journal of Forecasting 23(2): 115-139.

7.Cobb, M., 2009, "Forecasting Chilean Inflation from Disaggregate Components," Documento de Trabajo 545, Banco Central de Chile.

8.Fischer, S., 1981, "Relative Shocks, Relative Price Volatility, and Inflation," Brooking Papers on Economic Activity 2: 381-431.

9.Geweke, J., y R. Meese, 1981, "Estimating Regression Models of Finite but Unknown Order," International Economic Review 22(1): 55-70.

10.Giacomini, R., y H. White, 2006, "Tests of Conditional Predictive Ability," Econometrica 74(6): 1545-1578.

11.Gordon, R.J., y S.R. King, 1982, "The Output Cost of Desinflation in Traditional and Vector Autoregressive Models," Brooking Papers on Economic Activity 13(1): 205-244.

12.Granger, C.W.J, y Y. Jeon, 2004, "Forecasting Performance of Infomation Criteria with Many Macro Series," Journal of Applied Statistics 31(10): 1227-1240.

13.Granger, C.W.J., M. King, y H. White, 1995, "Comments on Testing Economic Theories and the use of Model Selection Criteria," Journal of Econometrics 67(1): 173-187.

14.Holan, S.H., R. Lund, y G. Davis, 2010, "The ARMA Alphabet Soup: A Tour of ARMA Model Variants," Statistics Surveys 4: 232-274.

15.Hurvich, C.M., y C.-L. Tsai, 1993, "A Corrected Akaike Information Criterion for Vector Autoregressive Model Selection," Journal of Time Series Analysis 14: 271-279.

16.Kilian, L., 2001, "Impulse Response Analysis in Vector Autoregressions with Unknown Lag Order," Journal of Forecasting 20(3): 161-179.

17.Kullback, S., y R.A. Leibler, 1951, "On Information and Sufficiency," Annals of Mathematical Statistics 22: 79-86.

18.Lütkepohl, H., 1985, "Comparison of Criteria for Estimating the Order of a Vector Autoregressive Process," Journal of Time Series Analysis 6(1): 35-52.

19.Medel, C.A., 2012, "How Informative are the In-Sample Information Criteria to Forecasting? The Chilean GDP Case," manuscrito, Banco Central de Chile.

20.Mélard, G., y J.-M. Pasteels, 2000, "Automatic ARIMA Modeling Including Interventions, using Time Series Expert Software," International Journal of Forecasting 16(4): 497-508.

21.Miller, P., T.M. Supel, y T.H. Turner, 1980, "Estimating the Effects of the Oil-Price Shock," Quarterly Review, Federal Reserve Bank of Minneapolis.

22.Newey, W., y K. West, 1987, "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica 55(3): 703-708.

23.Nickelsburg, G., 1985, "Small-Sample Properties of Dimensionality Statistics for Fitting VAR Models to Aggregate Economic Data -A Monte Carlo Study," Journal of Econometrics 28(2): 183-192.

24.Nishi, R., 1988, "Maximum Likelihood Principle and Model Selection when the True Model is Unspecified," Journal Multivariate Analysis 27: 392-403.

25.Pincheira, P., y A. García, 2009, "Forecasting Inflation in Chile with an Accurate Benchmark," Documento de Trabajo 514, Banco Central de Chile.

26.Poskitt, D.S., 1994, "A Note on Autoregressive Modelling," Econometric Theory 10: 884-890.

27.Pötscher, B.M., y S. Srinivasan, 1991, "A Comparison of Order Estimation Procedures for ARMA Models," Statistica Sinica 4: 29-50.

28.Raffalovich, L.E., G.D. Deane, D. Armstrong, y H.-S. Tsao, 1980, "Model Selection Procedures in Social Research: Monte-Carlo Simulation Results," Journal of Applied Statistics 35(10): 1094-1114.

29.Rissasen, J., 1978, "Modeling by Shortest Data Description," Automatica 14(5): 465-471.

30.Sargent, T., y C. Sims, 1977, "Business Cycle Modeling Without Pretending to Have too Much a priori Economic Theory," Working Papers 55, Federal Reserve Bank of Minneapolis.

31.Sawa, T., 1978, "Information Criteria for Discriminating Among Alternative Regression Models," Econometrica 46(6): 1273-1282.

32.Sawyer, K.R., 1979, "Model Selection Criteria: Parsimony in the Context of Nested Models," Australian National University, Canberra.

33.Salau, M.O., 2002, "On the Accuracy and Asymptotic Convergence of Widely used Estimators for Autoregressive Models", Advances in Modeling and Analysis 2: 1-18.

34.Schwarz, G.E., 1978, "Estimating the Dimension of a Model," Annals of Statistics 6(2): 461-464.

35.Shibata, R., 1976, "Selection of the Order of and Autoregressive Model by Akaike Information Criterion," Biometrika 63(1): 117-126.

36.Shibata, R., 1980, "Asymptotically Efficient Selection of the Order of the Model for Estimating Parameters of a Linear Process," Annals of Statistics 8(1): 147-164.

37.Shittu, O.I., y M.J. Asemota, 2009, "Comparison of Criteria for Estimating the Order of Autoregressive Process: A Monte Carlo Approach," European Journal of Scientific Research 30(3): 409-416.

38.Sims, C., 1980, "Macroeconomics and Reality," Econometrica 48(1): 1-48.

39.Sin, C.Y., y H. White, 1996, "Information Criteria for Selecting Possibly Misspecified Parametric Models," Journal of Econometrics 71: 207-223.

40.Stock, J., y M. Watson, 2007, A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series, en Engle, R.E. y H. White (eds.), Cointegration, Causality, and Forecasting: A Fetstschrift in Honour of Clive W.J. Granger, Oxford University Press.

41.Stone, M., 1979, "Comments on Model Selection Criteria of Akaike and Schwarz," Journal of the Royal Statistical Society -Series B (Methodological) 41(2): 276-278.

42.Yi, G., y G. Judge, 1988, "Statistical Model Selection Criteria," Economic Letters 28(1): 47-51.