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Goodness of fit tests in random coefficient regression models

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  • Delicado, Pedro
  • Romo, Juan

Abstract

Random coefficient regressions have been applied in a wide range of fields, from biology to economics, and constitute a common frame for several important statistical models. A nonparametric approach to inference in random coefficient models was initiated by Beran and Hall. In this paper we introduce and study goodness of fit tests for the coefficient distributions; their asymptotic behaviour under the null hypothesis is obtained. We also propose bootstrap resampling strategies to approach these distributions and prove their asymptotic validity using results by Gine and Zinn on bootstrap empirical processes. A simulation study illustrates the properties of these tests.

Suggested Citation

  • Delicado, Pedro & Romo, Juan, 1994. "Goodness of fit tests in random coefficient regression models," DES - Working Papers. Statistics and Econometrics. WS 3962, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:3962
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    Cited by:

    1. Pedro Delicado & Juan Romo, 1998. "Constant coefficient tests for random coefficient regression," Economics Working Papers 329, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Tianshun Yan & Changlin Mei, 2017. "A test for a parametric form of the volatility in second-order diffusion models," Computational Statistics, Springer, vol. 32(4), pages 1583-1596, December.
    3. Zhang, Chun-Xia & Mei, Chang-Lin & Zhang, Jiang-She, 2007. "An empirical study of a test for polynomial relationships in randomly right censored regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6543-6556, August.
    4. Delicado, Pedro & Romo, Juan, 1995. "Random coefficient regressions: parametric goodness of fit tests," DES - Working Papers. Statistics and Econometrics. WS 4199, Universidad Carlos III de Madrid. Departamento de Estadística.

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