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M-Testing Using Finite and Infinite Dimensional Parameter Estimators

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Author Info
Halbert White
Yongmiao Hong

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Abstract

The m-testing approach provides a general and convenient framework in which to view and construct specification tests for econometric models. Previous m-testing frameworks only consider test statistics that involve finite dimensional parameter estimators and infinite dimensional parameter estimators affecting the limit distribution of the m-test statistics. In this paper we propose a new m-testing framework using both finite and infinite dimensional parameter estimators, where the latter may or may not affect the limit distribution of the m-test. This greatly extends the potential and flexibility of m-testing. The new m-testing framework can be used to test hypotheses on parametric, semiparametric and nonparametric models. Some examples are given to illustrate how to use it to develop new specification tests.

* Cornell University

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Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number 93-01r.

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Date of creation: Jan 1999
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Handle: RePEc:cdl:ucsdec:93-01r

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  1. Wu, De-Min, 1973. "Alternative Tests of Independence Between Stochastic Regressors and Disturbances," Econometrica, Econometric Society, vol. 41(4), pages 733-50, July. [Downloadable!] (restricted)
  2. Andrews, Donald W K, 1991. "Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models," Econometrica, Econometric Society, vol. 59(2), pages 307-45, March. [Downloadable!] (restricted)
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  3. Tauchen, George, 1985. "Diagnostic testing and evaluation of maximum likelihood models," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 415-443. [Downloadable!] (restricted)
  4. Lavergne, P. & Vuong, Q., 1992. "Nonparametric Selection of Regressors : the Nonnested Case," Papers 9204, Southern California - Department of Economics.
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  5. Donald W.K. Andrews, 1989. "An Empirical Process Central Limit Theorem for Dependent Non-Identically Distributed Random Variables," Cowles Foundation Discussion Papers 907, Cowles Foundation, Yale University. [Downloadable!]
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  6. Robinson, P M, 1991. "Consistent Nonparametric Entropy-Based Testing," Review of Economic Studies, Blackwell Publishing, vol. 58(3), pages 437-53, May. [Downloadable!] (restricted)
  7. Whang, Yoon-Jae & Andrews, Donald W. K., 1993. "Tests of specification for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 277-318. [Downloadable!] (restricted)
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  8. Newey, Whitney K., 1994. "Kernel Estimation of Partial Means and a General Variance Estimator," Econometric Theory, Cambridge University Press, vol. 10(02), pages 1-21, June. [Downloadable!]
  9. repec:cup:etheor:v:10:y:1994:i:2:p:233-53 is not listed on IDEAS
  10. Hall, Peter, 1984. "Central limit theorem for integrated square error of multivariate nonparametric density estimators," Journal of Multivariate Analysis, Elsevier, vol. 14(1), pages 1-16, February. [Downloadable!] (restricted)
  11. Stoker, Thomas M, 1989. "Tests of Additive Derivative Constraints," Review of Economic Studies, Blackwell Publishing, vol. 56(4), pages 535-52, October. [Downloadable!] (restricted)
  12. Geweke, John, 1981. "The Approximate Slopes of Econometric Tests," Econometrica, Econometric Society, vol. 49(6), pages 1427-42, November. [Downloadable!] (restricted)
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  1. Natércia Fortuna, 2004. "Local rank tests in a multivariate nonparametric relationship," FEP Working Papers 137, Universidade do Porto, Faculdade de Economia do Porto. [Downloadable!]
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  2. Liangjun Su & Halbert White, 2003. "A Consistent Characteristic-Fuction-Based Test for Conditional Independence," University of California at San Diego, Economics Working Paper Series 2003-11, Department of Economics, UC San Diego. [Downloadable!]
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