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

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  • Medel, Carlos A.

Abstract

There is no standard economic forecasting procedure that systematically outperforms the others at all horizons and with any dataset. A common way to proceed, in many contexts, is to choose the best model within a family based on a fitting criteria, and then forecast. I compare the out-of-sample performance of a large number of autoregressive integrated moving average (ARIMA) models with some variations, chosen by three commonly used information criteria for model building: Akaike, Schwarz, and Hannan-Quinn. I perform this exercise to identify how to achieve the smallest root mean squared forecast error with models based on information criteria. I use the Chilean GDP dataset, estimating with a rolling window sample to generate one- to four-step ahead forecasts. Also, I examine the role of seasonal adjustment and the Easter effect on out-of-sample performance. After the estimation of more than 20 million models, the results show that Akaike and Schwarz are better criteria for forecasting purposes where the traditional ARMA specification is preferred. Accounting for the Easter effect improves the forecast accuracy only with seasonally adjusted data, and second-order stationarity is best.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 35949.

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Date of creation: 14 Jan 2012
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Handle: RePEc:pra:mprapa:35949

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Keywords: data mining; forecasting; ARIMA; seasonal adjustment; Easter-effect;

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Cited by:
  1. 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.
  2. Carlos Medel, 2012. "¿Akaike o Schwarz? ¿Cuál elegir para Predecir el PIB Chileno?," Working Papers Central Bank of Chile, Central Bank of Chile 658, Central Bank of Chile.
  3. 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, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 461-515, octubre-d.
  4. Javier Pereda, 2011. "Estimación de la tasa natural de interés para Perú: un enfoque financiero," Monetaria, Centro de Estudios Monetarios Latinoamericanos, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 429-459, octubre-d.
  5. Daniel Fernández, 2011. "Suficiencia del capital y previsiones de la banca uruguaya por su exposición al sector industrial," Monetaria, Centro de Estudios Monetarios Latinoamericanos, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 517-589, octubre-d.
  6. Medel, Carlos A., 2014. "Probabilidad Clásica de Sobreajuste con Criterios de Información: Estimaciones con Series Macroeconómicas Chilenas
    [Classical Probability of Overfitting with Information Criteria: Estimations wi
    ," MPRA Paper 57401, University Library of Munich, Germany.
  7. Carlos A. Medel Vera, 2011. "¿Akaike o Schwarz? ¿Cuál utilizar para predecir el PIB chileno?," Monetaria, Centro de Estudios Monetarios Latinoamericanos, Centro de Estudios Monetarios Latinoamericanos, vol. 0(4), pages 591-615, octubre-d.

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