Markus Frölich

Programme Evaluation and Treatment Choice (eBook, PDF)

• Format: PDF

Produktbeschreibung

Policy evaluation and programme choice are important tools for informed decision-making, for the administration of active labour market programmes, training programmes, tuition subsidies, rehabilitation programmes etc. Whereas the evaluation of programmes and policies is mainly concerned with an overall assessment of impact, benefits and costs, programme choice considers an optimal allocation of individuals to the programmes. This book surveys potential evaluation strategies for policies with multiple programmes and discusses evaluation and treatment choice in a coherent framework. Recommendations for choosing appropriate evaluation estimators are derived. Furthermore, a semiparametric estimator of optimal treatment choice is developed to assist in the optimal allocation of participants.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 194
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9783642557163
• Artikelnr.: 53172992

Autorenporträt

Markus Frölich, University of St. Gallen, Switzerland

Inhaltsangabe

1 Introduction.- 2 Programme Evaluation and Treatment Choice - An Overview.- 2.1 Introduction in Programme Evaluation.- 2.2 Optimal Treatment Choice.- 2.3 Nonparametric Regression.- 3 Nonparametric Covariate Adjustment in Finite Samples.- 3.1 Potential Efficiency Gains of Local Polynomial Matching.- 3.2 Approximation to the MSE and Bandwidth Choice.- 3.3 Data-driven Bandwidth Choice by Cross-Validation.- 3.4 Matching with Unknown Propensity Score.- 4 Semiparametric Estimation of Optimal Treatment Choices.- 4.1 Estimation of Conditional Expected Potential Outcomes.- 4.2 Optimal Choice and Swedish Rehabilitation Programmes.- 5 Conclusions.- A Appendix.- B Appendix.- C Appendix.- MSE-Approximation for Local Polynomial Matching.- Additional Tables to Chapter 3.- D Appendix.- D.1 Simulated Mean Squared Error for Sample Size 40.- D.2 Simulated Mean Squared Error for Sample Size 200.- D.3 Simulated Mean Squared Error for Sample Size 1000.- D.4 MSE Approximation: Kernel Matching, Sample Size 200.- D.5 MSE Approximation: Kernel Matching, Sample Size 1000.- D.6 MSE Approximation: Local Linear, Sample Size 200.- D.7 MSE Approximation: Local Linear, Sample Size 1000.- E Appendix.- E.1 Asymptotic Properties of the GMM Estimator.- E.2 Power of the J-tests - Additional Monte Carlo Results.- E.3 Additional Tables to Swedish Rehabilitation Programmes.- References.