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Data-Driven Model Evaluation: A Test for Revealed Performance

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Author Info

  • Jeffrey S. Racine
  • Christopher F. Parmeter

Abstract

When comparing two competing approximate models using a particular loss function, the one having smallest `expected true error' for that loss function is expected to lie closest to the underlying data generating process (DGP) given this loss function and is therefore to be preferred. In this chapter we consider a data-driven method for testing whether or not two competing approximate models are equivalent in terms of their expected true error (i.e., their expected performance on unseen data drawn from the same DGP). The proposed test is quite flexible with regards to the types of models that can be compared (i.e., nested versus non-nested, parametric versus nonparametric) and is applicable in cross-sectional and time-series settings. Moreover, in time-series settings our method overcomes two of the drawbacks associated with dominant approaches, namely, their reliance on only one split of the data and the need to have a suciently large `hold-out' sample for these tests to possess adequate power.

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File URL: http://socserv.mcmaster.ca/econ/rsrch/papers/archive/2012-13.pdf
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Bibliographic Info

Paper provided by McMaster University in its series Department of Economics Working Papers with number 2012-13.

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Length: 35 pages
Date of creation: Oct 2012
Date of revision:
Handle: RePEc:mcm:deptwp:2012-13

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Related research

Keywords: approximate; misspecified; model selection; predictive accuracy; data mining;

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References

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  1. Maasoumi, Esfandiar & Racine, Jeff & Stengos, Thanasis, 2007. "Growth and convergence: A profile of distribution dynamics and mobility," Journal of Econometrics, Elsevier, vol. 136(2), pages 483-508, February.
  2. Tristen Hayfield & Jeffrey S. Racine, . "Nonparametric Econometrics: The np Package," Journal of Statistical Software, American Statistical Association, vol. 27(i05).
  3. Daniel J. Henderson & Christopher F. Parmeter & Subal C. Kumbhakar, 2007. "Nonparametric estimation of a hedonic price function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 695-699.
  4. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, October.
  5. Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
  6. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
  7. Wooldridge, Jeffrey M., 1992. "A Test for Functional Form Against Nonparametric Alternatives," Econometric Theory, Cambridge University Press, vol. 8(04), pages 452-475, December.
  8. Leeb, Hannes & Pötscher, Benedikt M., 2005. "Can One Estimate the Unconditional Distribution of Post-Model-Selection Estimators ?," MPRA Paper 72, University Library of Munich, Germany.
  9. Mc Cracken, Michael W., 2000. "Robust out-of-sample inference," Journal of Econometrics, Elsevier, vol. 99(2), pages 195-223, December.
  10. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
  11. Cheng Hsiao & Qi Li & Jeff Racine, 2006. "A Consistent Model Specification Test with Mixed Discrete and Continuous Data," IEPR Working Papers 06.47, Institute of Economic Policy Research (IEPR).
  12. Harry Haupt & Joachim Schnurbus & Rolf Tschernig, 2010. "On nonparametric estimation of a hedonic price function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 894-901.
  13. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  14. Meade, Nigel, 2002. "A comparison of the accuracy of short term foreign exchange forecasting methods," International Journal of Forecasting, Elsevier, vol. 18(1), pages 67-83.
  15. Davidson, R. & Mackinnon, J.G., 1997. "Bootstrap Tests of Nonnested Linear Regression Models," ASSET - Instituto De Economia Publica 170, ASSET (Association of Southern European Economic Theorists).
  16. Liu, Zhenjuan & Stengos, Thanasis, 1999. "Non-linearities in Cross-Country Growth Regressions: A Semiparametric Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 527-38, Sept.-Oct.
  17. Rob J. Hyndman & Anne B. Koehler, 2005. "Another Look at Measures of Forecast Accuracy," Monash Econometrics and Business Statistics Working Papers 13/05, Monash University, Department of Econometrics and Business Statistics.
  18. Dick Dijk & Philip Hans Franses, 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 727-744, December.
  19. Corradi, V. & Swanson, N.R., 2000. "A Consistent Test for Nonlinear Out of Sample Predictive Accuracy," Discussion Papers 0012, Exeter University, Department of Economics.
  20. Daniel J. Henderson & Chris Papageorgiou & Christopher F. Parmeter, 2012. "Growth Empirics without Parameters," Economic Journal, Royal Economic Society, vol. 122(559), pages 125-154, 03.
  21. Racine, Jeffrey, 2001. "On the Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 380-82, July.
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Citations

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Cited by:
  1. Bontemps, Christophe & Racine, Jeffrey S. & Simioni, Michel, 2011. "Nonparametric vs parametric binary choice models: An empirical investigation," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 116005, European Association of Agricultural Economists.
  2. Richard A. Ashley & Christopher F. Parmeter, 2013. "Sensitivity Analysis of Inference in GMM Estimation With Possibly-Flawed Moment Conditions," Working Papers e07-40, Virginia Polytechnic Institute and State University, Department of Economics.
  3. Steven F. Koch & Jeffrey S. Racine, 2013. "Health Care Facility Choice and User Fee Abolition: Regression Discontinuity in a Multinomial Choice Setting," Working Papers 201353, University of Pretoria, Department of Economics.
  4. Kajal Lahiri & Liu Yang, 2012. "Forecasting Binary Outcomes," Discussion Papers 12-09, University at Albany, SUNY, Department of Economics.
  5. Asaftei, Gabriel & Parmeter, Christopher F., 2010. "Market power, EU integration and privatization: The case of Romania," Journal of Comparative Economics, Elsevier, vol. 38(3), pages 340-356, September.

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