Semiparametric Estimation of the Box-Cox Model Preliminary and Incomplete
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.
|Date of creation:||Jan 2001|
|Date of revision:|
|Contact details of provider:|| Postal: University of Iowa, Department of Economics, Henry B. Tippie College of Business, Iowa City, Iowa 52242|
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- Amemiya, Takeshi, 1974. "The nonlinear two-stage least-squares estimator," Journal of Econometrics, Elsevier, vol. 2(2), pages 105-110, July.
- Powell, James L., 1996. "Rescaled methods-of-moments estimation for the Box-Cox regression model," Economics Letters, Elsevier, vol. 51(3), pages 259-265, June.
- Amemiya, Takeshi & Powell, James L., 1981. "A comparison of the Box-Cox maximum likelihood estimator and the non-linear two-stage least squares estimator," Journal of Econometrics, Elsevier, vol. 17(3), pages 351-381, December.
- Khazzoom, J. Daniel, 1989. "A note on the application of the nonlinear two-stage least-squares estimator to a Box-Cox-transformed model," Journal of Econometrics, Elsevier, vol. 42(3), pages 377-379, November.
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