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A Consistent Test Of Conditional Parametric Distributions

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  • Zheng, John Xu
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    Abstract

    This paper proposes a new nonparametric test for conditional parametric distribution functions based on the first-order linear expansion of the Kullback Leibler information function and the kernel estimation of the underlying distributions. The test statistic is shown to be asymptotically distributed standard normal under the null hypothesis that the parametric distribution is correctly specified, whereas asymptotically rejecting the null with probability one if the parametric distribution is misspecified. The test is also shown to have power against any local alternatives approaching the null at rates slower than the parametric rate n 1/2. The finite sample performance of the test is evaluated via a Monte Carlo simulation.

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    Bibliographic Info

    Article provided by Cambridge University Press in its journal Econometric Theory.

    Volume (Year): 16 (2000)
    Issue (Month): 05 (October)
    Pages: 667-691

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    Handle: RePEc:cup:etheor:v:16:y:2000:i:05:p:667-691_16

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    Cited by:
    1. Chen, Bin & Hong, Yongmiao, 2014. "A unified approach to validating univariate and multivariate conditional distribution models in time series," Journal of Econometrics, Elsevier, vol. 178(P1), pages 22-44.
    2. Xu Zheng, 2012. "Testing parametric conditional distributions using the nonparametric smoothing method," Metrika, Springer, vol. 75(4), pages 455-469, May.
    3. Zheng, Xu, 2008. "Testing for discrete choice models," Economics Letters, Elsevier, vol. 98(2), pages 176-184, February.
    4. Li, Fuchun & Tkacz, Greg, 2006. "A consistent bootstrap test for conditional density functions with time-series data," Journal of Econometrics, Elsevier, vol. 133(2), pages 863-886, August.
    5. Fernandes, Marcelo, 2001. "Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes," Economics Working Papers (Ensaios Economicos da EPGE) 413, FGV/EPGE Escola Brasileira de Economia e Finan├žas, Getulio Vargas Foundation (Brazil).
    6. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    7. Fuchun Li & Greg Tkacz, 2001. "A Consistent Bootstrap Test for Conditional Density Functions with Time-Dependent Data," Working Papers 01-21, Bank of Canada.
    8. Juan Mora & Antonia Febrer, 2005. "Wage Distribution In Spain, 1994-1999: An Application Of A Flexible Estimator Of Conditional Distributions," Working Papers. Serie EC 2005-04, Instituto Valenciano de Investigaciones Econ├│micas, S.A. (Ivie).
    9. Delgado, Miguel A. & Stute, Winfried, 2008. "Distribution-free specification tests of conditional models," Journal of Econometrics, Elsevier, vol. 143(1), pages 37-55, March.
    10. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    11. Stefania D'Amico, 2005. "Density selection and combination under model ambiguity: an application to stock returns," Finance and Economics Discussion Series 2005-09, Board of Governors of the Federal Reserve System (U.S.).
    12. Wu, Edmond H.C. & Yu, Philip L.H. & Li, W.K., 2009. "A smoothed bootstrap test for independence based on mutual information," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2524-2536, May.
    13. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    14. Febrer, Antonia & Mora, Juan, 2009. "Flexible estimation of wage distributions in the presence of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2189-2200, April.
    15. repec:wyi:wpaper:002024 is not listed on IDEAS

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