How Informative are In–Sample Information Criteria to Forecasting? The Case of Chilean GDP
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.
|Date of creation:||Jan 2012|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (562) 670 2000
Fax: (562) 698 4847
Web page: http://www.bcentral.cl/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Pincheira, Pablo, 2013. "A Bunch of Models, a Bunch of Nulls and Inference about Predictive Ability," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 26-43, October.
- 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.
- 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.
- 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.
- Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
- Raffaella Giacomini & Halbert White, 2003.
"Tests of conditional predictive ability,"
Boston College Working Papers in Economics
572, Boston College Department of Economics.
- Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
- Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
- 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.
- 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.
- Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
- Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
- 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.
- 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.
- Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
- Ghysels, Eric & Osborn, Denise R. & Rodrigues, Paulo M.M., 2006. "Forecasting Seasonal Time Series," Handbook of Economic Forecasting, Elsevier.
- Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
- 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.
- Todd E. Clark, 2004. "Can out-of-sample forecast comparisons help prevent overfitting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.
- Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile 545, Central Bank of Chile.
- 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.
- 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.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:chb:bcchwp:657. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Claudio Sepulveda)
If references are entirely missing, you can add them using this form.