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New Goodness-of-fit Diagnostics for Conditional Discrete Response Models

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  • Igor Kheifets

    (New Economic School, Moscow)

  • Carlos Velasco

    (Dept. of Economics, Universidad Carlos III de Madrid)

Abstract

This paper proposes new specification tests for conditional models with discrete responses. In particular, we can test the static and dynamic ordered choice model specifications, which is key to apply efficient maximum likelihood methods, to obtain consistent estimates of partial effects and to get appropriate predictions of the probability of future events. The traditional approach is based on probability integral transforms of a jittered discrete data which leads to continuous uniform iid series under the true conditional distribution. We investigate in this paper an alternative transformation based only on original discrete data. We show analytically and in simulations that our approach dominates the traditional approach in terms of power. We apply the new tests to models of the monetary policy conducted by the Federal Reserve.

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Bibliographic Info

Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1924.

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Length: 33 pages
Date of creation: Nov 2013
Date of revision:
Handle: RePEc:cwl:cwldpp:1924

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Related research

Keywords: Specification tests; Count data; Dynamic discrete choice models; Conditional probability integral transform;

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  1. J.A.F. Machado & J. M. C. Santos Silva, 2003. "Quantiles for Counts," Econometrics 0303001, EconWPA.
  2. Startz, Richard, 2008. "Binomial Autoregressive Moving Average Models With an Application to U.S. Recessions," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 1-8, January.
  3. Donald W. K. Andrews, 1997. "A Conditional Kolmogorov Test," Econometrica, Econometric Society, vol. 65(5), pages 1097-1128, September.
  4. Delgado, Miguel A. & Carlos Escanciano, J., 2007. "Nonparametric tests for conditional symmetry in dynamic models," Journal of Econometrics, Elsevier, vol. 141(2), pages 652-682, December.
  5. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
  6. James D. Hamilton & Oscar Jorda, . "A model for the federal funds rate target," Department of Economics 99-07, California Davis - Department of Economics.
  7. Mora, Juan & Moro-Egido, Ana I., 2008. "On specification testing of ordered discrete choice models," Journal of Econometrics, Elsevier, vol. 143(1), pages 191-205, March.
  8. Jung, Robert C. & Kukuk, Martin & Liesenfeld, Roman, 2006. "Time series of count data: modeling, estimation and diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2350-2364, December.
  9. Igor Kheifets & Carlos Velasco, 2012. "Model Adequacy Checks for Discrete Choice Dynamic Models," Working Papers w0170, Center for Economic and Financial Research (CEFIR).
  10. Jushan Bai, 2003. "Testing Parametric Conditional Distributions of Dynamic Models," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 531-549, August.
  11. Winfried Stute, 2002. "Model Checks for Generalized Linear Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 29(3), pages 535-545.
  12. Giacomini, Raffaella & Politis, Dimitris N. & White, Halbert, 2013. "A Warp-Speed Method For Conducting Monte Carlo Experiments Involving Bootstrap Estimators," Econometric Theory, Cambridge University Press, vol. 29(03), pages 567-589, June.
  13. Dolado, Juan J & Maria-Dolores, Ramon, 2002. " Evaluating Changes in the Bank of Spain's Interest Rate Target: An Alternative Approach Using Marked Point Processes," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(2), pages 159-82, May.
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