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Estimating average partial effects under conditional moment independence assumptions

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  • Jeffrey M. Wooldridge

Abstract

I show how to identify and estimate the average partial effect of explanatory variables in a model where unobserved heterogeneity interacts with the explanatory variables and may be unconditionally correlated with the explanatory variables. To identify the populationaveragedeffects, I use extensions of ignorability assumptions that are used for estimating linear models with additive heterogeneity and forestimating average treatment effects. New stimators are obtained for estimating the unconditional average partial effect as well as the average partial effect conditional on functions of observed covariates.

Suggested Citation

  • Jeffrey M. Wooldridge, 2004. "Estimating average partial effects under conditional moment independence assumptions," CeMMAP working papers 03/04, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:03/04
    DOI: 10.1920/wp.cem.2004.0304
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    References listed on IDEAS

    as
    1. Wooldridge, Jeffrey M., 1999. "Distribution-free estimation of some nonlinear panel data models," Journal of Econometrics, Elsevier, vol. 90(1), pages 77-97, May.
    2. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    3. Angrist, J.D., 1991. "Linear Instrumental Variables Estimation Of Average Treatment Effects In Nonlinear Models," Harvard Institute of Economic Research Working Papers 1542, Harvard - Institute of Economic Research.
    4. Wooldridge, Jeffrey M., 2003. "Further results on instrumental variables estimation of average treatment effects in the correlated random coefficient model," Economics Letters, Elsevier, vol. 79(2), pages 185-191, May.
    5. Vella, Francis & Verbeek, Marno, 1999. "Estimating and Interpreting Models with Endogenous Treatment Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 473-478, October.
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    7. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
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    11. Joshua D. Angrist, 1991. "Instrumental Variables Estimation of Average Treatment Effects in Econometrics and Epidemiology," NBER Technical Working Papers 0115, National Bureau of Economic Research, Inc.
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    Cited by:

    1. DiTraglia, Francis J. & García-Jimeno, Camilo & O’Keeffe-O’Donovan, Rossa & Sánchez-Becerra, Alejandro, 2023. "Identifying causal effects in experiments with spillovers and non-compliance," Journal of Econometrics, Elsevier, vol. 235(2), pages 1589-1624.
    2. Destefanis, Sergio & Rehman, Naqeeb Ur, 2023. "Investment, innovation activities and employment across European regions," Structural Change and Economic Dynamics, Elsevier, vol. 65(C), pages 474-490.

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