Semiparametric estimation of conditional mean functions with missing data
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A new semiparametric estimator for estimating conditional expectation functions from incomplete data is proposed, which integrates parametric regression with nonparametric matching estimators. Besides its applicability to missing data situations due to non-response or attrition, the estimator can also be used for analyzing treatment effect heterogeneity and statistical treatment rules, where data on potential outcomes is missing by definition. By combining moments from a parametric specification with nonparametric estimates of mean outcomes in the non-responding population within a GMM framework, the estimator seeks to balance a good fit in the responding population with low bias in the non-responding population. The estimator is applied to analyzing treatment effect heterogeneity among Swedish rehabilitation programmes.
KeywordsMean Square Error Propensity Score Propensity Score Match Vocational Rehabilitation Average Bias
The author is also affiliated with the Institute for the Study of Labor (IZA), Bonn. I am grateful for discussions and comments to Bo Honoré, Francois Laisney, Michael Lechner, Ruth Miquel, Oivind Nilsen, Jeff Smith, the editor and three anonymous referees. This research was supported by the Swiss National Science Foundation (project NSF 4043-058311) and the Grundlagenforschungsfonds HSG (project G02110112).
- Angrist J, Krueger A (1999) Empirical strategies in labor economics. In: Ashenfelter O, Card D (eds) The handbook of labor economics, III. North-Holland, New York, pp 1277–1366Google Scholar
- Barnow B, Cain G, Goldberger A (1981) Selection on observables. Evaluation Studies Review Annual 5:43–59Google Scholar
- Dehejia R (2004) Program evaluation as a decision problem. forthcoming in J EconGoogle Scholar
- Heckman J, Robb R (1985) Alternative methods for evaluating the impact of interventions. In: Heckman J, Singer B (eds) Longitudinal analysis of labour market data. Cambridge University Press, CambridgeGoogle Scholar
- Heckman J, LaLonde R, Smith J (1999) The economics and econometrics of active labour market programs. In: Ashenfelter O, Card D (eds) The handbook of labor economics, III. North-Holland, New York, pp 1865–2097Google Scholar
- Little R, Rubin D (1987) Statistical analysis with missing data. Wiley, New YorkGoogle Scholar
- Manski C (2000) Identification problems and decisions under ambiguity: empirical analysis of treatment response and normative analysis of treatment choice. J Econ 95:415–442Google Scholar
- Wald A (1950) Statistical decision functions. Wiley, New YorkGoogle Scholar