This paper investigates the finite sample performance of three semiparametric estimators of the Box-Cox model. Two of the semiparametric estimators are the nonlinear two-stage least squares (NL2SLS) estimator proposed by Amemiya and Powell (1981) and a rescaled version (RNL2SLS) proposed by Powell (1996) to eliminate an inconsistent minimizer of the NL2SLS objective function. These estimators are special cases of the general-method-of-moments (GMM) estimator. The third estimator is one recently proposed by Foster, Tian and Wei (FTW) (2000), which is based on the empirical distribution function of the dependent variable. The Monte Carlo results show that there is no best estimator across the experimental designs considered. The NL2SLS and RN2SLS estimators tend to suffer from near nonidentification due to a flat objective function, and, although the FTW estimator is better than NL2SLS and RNL2SLS for many designs, it can perform badly for certain designs. The results also show that first-order asymptotic theory may provide a poor approximation to the finite sample distributions of the estimators.
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Paper provided by University of Iowa, Department of Economics in its series Working Papers with number
2001-01.
Length: 20 pages Date of creation: Jan 2001 Date of revision: Handle: RePEc:uia:iowaec:2001-01
Contact details of provider: Postal: University of Iowa, Department of Economics, Henry B. Tippie College of Business, Iowa City, Iowa 52242 Phone: (319) 335-0829 Fax: (319) 335-1956 Web page: http://tippie.uiowa.edu/economics/ More information through EDIRC
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Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
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