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Data-Driven Model Evaluation: A Test for Revealed Performance

  • Jeffrey S. Racine
  • Christopher F. Parmeter

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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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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  1. Haerdle,Wolfgang & Marron,J., 1987. "Semiparametric comparision of regression curve," Discussion Paper Serie A 93, University of Bonn, Germany.
  2. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, June.
  3. Inoue, Atsushi & Kilian, Lutz, 2002. "In-sample or out-of-sample tests of predictability: which one should we use?," Working Paper Series 0195, European Central Bank.
  4. Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
  5. Hsiao, Cheng & Li, Qi & Racine, Jeffrey S., 2007. "A consistent model specification test with mixed discrete and continuous data," Journal of Econometrics, Elsevier, vol. 140(2), pages 802-826, October.
  6. van Dijk, D.J.C. & Franses, Ph.H.B.F., 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Econometric Institute Research Papers EI 2003-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  7. Valentina Corradi & Norman Swanson, 2003. "Some Recent Developments in Predictive Accuracy Testing With Nested Models and (Generic) Nonlinear Alternatives," Departmental Working Papers 200316, Rutgers University, Department of Economics.
  8. Davidson, Russell & MacKinnon, James G., 2002. "Bootstrap J tests of nonnested linear regression models," Journal of Econometrics, Elsevier, vol. 109(1), pages 167-193, July.
  9. Ashley, Richard, 1998. "A new technique for postsample model selection and validation," Journal of Economic Dynamics and Control, Elsevier, vol. 22(5), pages 647-665, May.
  10. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-67, July.
  11. 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.
  12. Maasoumi, Esfandiar & Racine, Jeff, 2006. "Growth And Convergence: A Profile Of Distribution Dynamics And Mobility," Departmental Working Papers 0605, Southern Methodist University, Department of Economics.
  13. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
  14. 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.
  15. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
  16. Mc Cracken, Michael W., 2000. "Robust out-of-sample inference," Journal of Econometrics, Elsevier, vol. 99(2), pages 195-223, December.
  17. 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.
  18. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
  19. Leeb, Hannes & P tscher, Benedikt M., 2008. "Can One Estimate The Unconditional Distribution Of Post-Model-Selection Estimators?," Econometric Theory, Cambridge University Press, vol. 24(02), pages 338-376, April.
  20. 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.
  21. Wooldridge, Jeffrey M., 1992. "A Test for Functional Form Against Nonparametric Alternatives," Econometric Theory, Cambridge University Press, vol. 8(04), pages 452-475, December.
  22. Haupt, Harry & Schnurbus, Joachim & Tschernig, Rolf, 2008. "On Nonparametric Estimation of a Hedonic Price Function," University of Regensburg Working Papers in Business, Economics and Management Information Systems 429, University of Regensburg, Department of Economics.
  23. Hardle, W. & Mammen, E., 1990. "Comparing nonparametric versus parametric regression fits," CORE Discussion Papers 1990065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  24. 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.
  25. Qi Gao & Long Liu & Jeffrey S. Racine, 2015. "A Partially Linear Kernel Estimator for Categorical Data," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 959-978, December.
  26. 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.
  27. 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.
  28. 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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