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Semiparametric Specification Testing

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Author Info

  • Miguel A. Delgado
  • Thanasis Stengos

Abstract

We propose a specification test of a parametrically specified model against a weakly specified alternative. The latter is estimated using K nonparametric nearest neighbors (K-NN) in the context of an artificial regression. We derived the asymptotic distribution under the null hypothesis and under a series of local alternatives. Monte Carlo simulations suggest that the test is quite powerful although it has a tendency to over-reject under the null hypothesis.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_778.pdf
File Function: First version 1990
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Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 778.

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Date of creation: Mar 1990
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Handle: RePEc:qed:wpaper:778

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Related research

Keywords: K-nearest neighbor regression; artificial nesting; nonested hypothesis;

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Cited by:
  1. Alev Atak & Oliver Linton & Zhijie Xiao, 2011. "A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom," Post-Print peer-00844810, HAL.
  2. Russell Davidson & James G. MacKinnon, 1999. "Artificial Regressions," Working Papers, Queen's University, Department of Economics 978, Queen's University, Department of Economics.
  3. Yanqin Fan & Qi Li, 1995. "Bootstrapping J-type tests for non-nested regression models," Economics Letters, Elsevier, vol. 48(2), pages 107-112, May.
  4. Miguel A. Delgado & Juan Mora, 1995. "On asymptotic inferences in non-parametric and semiparametric models with discrete and mixed regressors," Investigaciones Economicas, Fundación SEPI, Fundación SEPI, vol. 19(3), pages 435-467, September.

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