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How informative are in-sample information criteria to forecasting? The case of Chilean GDP

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  • Carlos A. Medel

Abstract

This paper compares out-of-sample performance, using the Chilean GDP dataset, of a large number of autoregressive integrated moving average (ARIMA) models with some variations to identify how to achieve the smallest root mean squared forecast error with models based on information criteria--Akaike, Schwarz, and Hannan-Quinn. The analysis also addresses the role of seasonal adjustment and the Easter effect. The results show that Akaike and Schwarz are better criteria for forecasting when using actual series and Schwarz and Hannan-Quinn are better with seasonally adjusted data. Accounting for the Easter effect improves forecast accuracy for actual and seasonally adjusted data.

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File URL: http://www.economia.puc.cl/docs/107764_laje_501133.pdf
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Bibliographic Info

Article provided by Instituto de Economía. Pontificia Universidad Católica de Chile. in its journal Latin American Journal of Economics-formerly Cuadernos de Economia.

Volume (Year): 50 (2013)
Issue (Month): 1 (May)
Pages: 133-161

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Handle: RePEc:ioe:cuadec:v:50:y:2013:i:1:p:133-161

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Keywords: Data mining; forecasting; ARIMA; seasonal adjustment; Easter effect;

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References

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  1. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  2. 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.
  3. Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
  4. Rob J. Hyndman & Roman A. Ahmed & George Athanasopoulos, 2007. "Optimal combination forecasts for hierarchical time series," Monash Econometrics and Business Statistics Working Papers 9/07, Monash University, Department of Econometrics and Business Statistics.
  5. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
  6. Pablo Pincheira, 2011. "A Bunch of Models, a Bunch of Nulls and Inference About Predictive Ability," Working Papers Central Bank of Chile 607, Central Bank of Chile.
  7. Marcus Cobb C. & Carlos A. Medel V., 2010. "Una Estimación del Impacto del Efecto Calendario en Series Desestacionalizadas Chilenas de Actividad y Demanda," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 13(3), pages 95-103, December.
  8. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-52, April.
  9. 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.
  10. Todd E. Clark, 2000. "Can out-of-sample forecast comparisons help prevent overfitting?," Research Working Paper RWP 00-05, Federal Reserve Bank of Kansas City.
  11. Carlos Medel & Marcela Urrutia, 2010. "Proyección Agregada y Desagregada del PIB Chileno con Procedimientos Automatizados de Series de Tiempo," Working Papers Central Bank of Chile 577, Central Bank of Chile.
  12. Dickey, David A & Pantula, Sastry G, 1987. "Determining the Ordering of Differencing in Autoregressive Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 455-61, October.
  13. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
  14. Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile 545, Central Bank of Chile.
  15. Ghysels, Eric & Osborn, Denise R. & Rodrigues, Paulo M.M., 2006. "Forecasting Seasonal Time Series," Handbook of Economic Forecasting, Elsevier.
  16. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  17. Capistrán, Carlos & Constandse, Christian & Ramos-Francia, Manuel, 2010. "Multi-horizon inflation forecasts using disaggregated data," Economic Modelling, Elsevier, vol. 27(3), pages 666-677, May.
  18. Pablo Pincheira Brown & Álvaro García Marín, 2009. "Forecasting Inflation in Chile With an Accurate Benchmark," Working Papers Central Bank of Chile 514, Central Bank of Chile.
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Citations

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Cited by:
  1. Daniel Fernández, 2011. "Suficiencia del capital y previsiones de la banca uruguaya por su exposición al sector industrial," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 517-589, octubre-d.
  2. Carlos A. Medel Vera, 2011. "¿Akaike o Schwarz? ¿Cuál utilizar para predecir el PIB chileno?," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 591-615, octubre-d.
  3. Medel, Carlos A., 2012. "¿Akaike o Schwarz? ¿Cuál elegir para predecir el PIB chileno?
    [Akaike or Schwarz? Which One is a Better Predictor of Chilean GDP?]
    ," MPRA Paper 35950, University Library of Munich, Germany.
  4. Medel, Carlos A., 2014. "A Comparison Between Direct and Indirect Seasonal Adjustment of the Chilean GDP 1986-2009 with X-12-ARIMA," MPRA Paper 57053, University Library of Munich, Germany.
  5. Javier Pereda, 2011. "Estimación de la tasa natural de interés para Perú: un enfoque financiero," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 429-459, octubre-d.
  6. Tamara Burdisso & Eduardo Ariel Corso, 2011. "Incertidumbre y dolarización de cartera: el caso argentino en el último medio siglo," Monetaria, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 461-515, octubre-d.

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