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A Bunch of Models, a Bunch of Nulls and Inference About Predictive Ability

  • Pablo Pincheira

Inference about predictive ability is usually carried out in the form of pairwise comparisons between two competing forecasting methods. Nevertheless, some interesting questions are concerned with families of models and not just with a couple of forecasting strategies. An example of this would be the question about the predictive accuracy of pure time-series models versus models based on economic fundamentals. It is clear that an appropriate answer to this question requires comparing families of models, which may include a number of different forecasting strategies. Another usual approach in the literature consists of comparing the accuracy of a new forecasting method with a natural benchmark. Nevertheless, unless the econometrician is completely sure about the superiority of the benchmark over the rest of the methods available in the literature, he/she may want to compare the accuracy of his/her new forecasting model, and its extensions, against a broader set of methods. In this article we present a simple methodology to test the null hypothesis of equal predictive ability between two families of forecasting methods. Our approach corresponds to a natural extension of the White (2000) reality check in which we allow for the families being compared to be populated by a large number of forecasting methods. We illustrate our testing approach with an empirical application comparing the ability of two families of models to predict headline inflation in Chile, the US, Sweden and Mexico. With this illustration we show that comparing families of models using the usual approach based on pairwise comparisons of the best ex-post performing models in each family, may lead to conclusions that are at odds with those suggested by our approach.

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Paper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 607.

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Date of creation: Jan 2011
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Handle: RePEc:chb:bcchwp:607
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  1. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
  2. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
  3. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  4. Andrew Atkeson & Lee E. Ohanian., 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
  5. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
  6. Dean Croushore, 2006. "An evaluation of inflation forecasts from surveys using real-time data," Working Papers 06-19, Federal Reserve Bank of Philadelphia.
  7. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  8. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  9. repec:att:wimass:9417 is not listed on IDEAS
  10. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September.
  11. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, Elsevier.
  12. Ghysels, Eric & Osborn, Denise R. & Rodrigues, Paulo M.M., 2006. "Forecasting Seasonal Time Series," Handbook of Economic Forecasting, Elsevier.
  13. Pincheira, Pablo & García, Álvaro, 2012. "En busca de un buen marco de referencia predictivo para la inflación en Chile," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(313), pages 85-123, enero-mar.
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