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Methods Based on Selection on Unobservables

  • Giovanni Cerulli
Chapter
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 49)

Abstract

This chapter covers econometric methods for estimating average treatment effects (ATEs) of social and economic programs under the assumption of “selection on unobservables”. When nonobservable factors significantly drive the nonrandom assignment to treatment, recovering consistent estimations of average treatment effects relying only on observables is no longer possible. As a consequence, econometric methods only based on assuming “selection on observables” become inappropriate. This chapter illustrates methods suitable for dealing with unobservable selection, thus critically discussing various Instrumental-variables (IV) approaches, by introducing the Heckman Selection-model, and by illustrating the Difference-in differences (DID) estimator both in a repeated cross section and in a longitudinal data structure. The chapter concludes by focusing on a number of applications of previous methods using built-in and user-written Stata commands on real and simulative datasets.

Keywords

Average Treatment Effects (ATE) Repeated Cross Sections Select Models (SM) Longitudinal Data Structure Heckit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Giovanni Cerulli
    • 1
  1. 1.Research Institute on Sustainable Economic GrowthCNR-IRCrES National Research Council of ItalyRomaItaly

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