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Sequential Matching Estimation of Dynamic Causal Models

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  • Michael Lechner

Abstract

This paper proposes sequential matching and inverse selection probability weighting to estimate dynamic casual effects. The sequential matching estimators extend simple, matching estimators based on propensity scores for static causal analysis that have been frequently applied in the evaluation literature. A Monte Carlo study shows that the suggested estimators perform well in small and medium seize samples. Based on the application of the sequential matching estimators to an empirical problem - an evaluation study of the Swiss active labour market policies - some implementational issues are discussed and results are provided.

Suggested Citation

  • Michael Lechner, 2004. "Sequential Matching Estimation of Dynamic Causal Models," University of St. Gallen Department of Economics working paper series 2004 2004-06, Department of Economics, University of St. Gallen.
    • Handle: RePEc:usg:dp2004:2004-06
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    More about this item

    Keywords

    Dynamic treatment effects; nonparametric identification; causal effects; sequential randomisation; programme evaluation; panel data;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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