A Simple Matching Method for Estimating Sample Selection Models Using Experimental Data
AbstractIn this paper estimation of sample selection models using experimental data is considered with some weak restriction imposed on the error distribution. Under a normality setting, the most popular approach is the two-step method proposed by Heckman (1979). But Heckman¡¯s approach relies on the nonlinearity of the probit function (i.e. the nonlinearity of the selection correction function) unless some exclusion restriction is imposed. Furthermore, Heckman¡¯s method is sensitive to the underlying distributional assumption. Following this two-step method, several semiparametric estimators have been proposed for sample selection models by explicitly imposing the exclusion restriction. Using experimental data, this paper proposes a simple semiparametric matching method. There are certain advantages of our estimator over Heckman¡¯s estimator and the existing semiparametric estimators under either the parametric setting and semiparametric setting. We do not rely on the nonlinearity of the selection correction function or the exclusion restriction. In addition, unlike other semiparametric methods, we can also estimate the intercept term in the equation of interest. The estimator is shown to be consistent and asymptotically normal under some regularity conditions. A small monte carlo study illustrates the usefulness of the new estimator.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Society for AEF in its journal Annals of Economics and Finance.
Volume (Year): 6 (2005)
Issue (Month): 1 (May)
Matching method; Experimental data;
Find related papers by JEL classification:
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
- C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
- Klein, Roger W & Spady, Richard H, 1993.
"An Efficient Semiparametric Estimator for Binary Response Models,"
Econometric Society, vol. 61(2), pages 387-421, March.
- Klein, R.W. & Spady, R.H., 1991. "An Efficient Semiparametric Estimator for Binary Response Models," Papers 70, Bell Communications - Economic Research Group.
- Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-94, July.
- Nawata, Kazumitsu, 1993. "A note on the estimation of models with sample-selection biases," Economics Letters, Elsevier, vol. 42(1), pages 15-24.
- Han, Aaron K., 1987. "Non-parametric analysis of a generalized regression model : The maximum rank correlation estimator," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 303-316, July.
- Cosslett, Stephen R, 1983. "Distribution-Free Maximum Likelihood Estimator of the Binary Choice Model," Econometrica, Econometric Society, vol. 51(3), pages 765-82, May.
- Andrews, Donald W K, 1991.
"Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models,"
Econometric Society, vol. 59(2), pages 307-45, March.
- Donald W.K. Andrews, 1988. "Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models," Cowles Foundation Discussion Papers 874R, Cowles Foundation for Research in Economics, Yale University, revised May 1989.
- Leung, S.F. & Yu, S., 1992.
"On the Choice Between Sample Selection and Two-Part Models,"
RCER Working Papers
337, University of Rochester - Center for Economic Research (RCER).
- Leung, Siu Fai & Yu, Shihti, 1996. "On the choice between sample selection and two-part models," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 197-229.
- Donald, Stephen G., 1995. "Two-step estimation of heteroskedastic sample selection models," Journal of Econometrics, Elsevier, vol. 65(2), pages 347-380, February.
- Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
- James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998.
"Characterizing Selection Bias Using Experimental Data,"
Econometric Society, vol. 66(5), pages 1017-1098, September.
- James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," NBER Working Papers 6699, National Bureau of Economic Research, Inc.
- Sherman, Robert P., 1994. "U-Processes in the Analysis of a Generalized Semiparametric Regression Estimator," Econometric Theory, Cambridge University Press, vol. 10(02), pages 372-395, June.
- Lee, Lung-Fei, 1994.
"Semiparametric instrumental variable estimation of simultaneous equation sample selection models,"
Journal of Econometrics,
Elsevier, vol. 63(2), pages 341-388, August.
- Lee, L-F., 1991. "Semiparametric Instrumental Variable Estimation of Simultaneous Equation Sample Selection Models," Papers 263, Minnesota - Center for Economic Research.
- James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
- LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-20, September.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Qiang Gao).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.