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Causal Effects of Monetary Shocks: Semiparametric Conditional Independence Tests with a Multinomial Propensity Score

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  • Joshua D. Angrist

    (Massachusetts Institute of Technology)

  • Guido M. Kuersteiner

    (Georgetown University)

Abstract

We develop semiparametric tests for conditional independence in time series models of causal effects. Our approach is motivated by empirical studies of monetary policy effects and is semiparametric in the sense that we model the process determining the distribution of treatment—the policy propensity score—but leave the model for outcomes unspecified. A conceptual innovation is that we adapt the cross-sectional potential outcomes framework to a time series setting. We also develop root-T consistent distribution-free inference methods for full conditional independence testing, appropriate for dependent data and allowing for first-step estimation of the (multinomial) propensity score. © 2011 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 93 (2011)
Issue (Month): 3 (August)
Pages: 725-747

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Handle: RePEc:tpr:restat:v:93:y:2011:i:3:p:725-747

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Web page: http://mitpress.mit.edu/journals/

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Web: http://mitpress.mit.edu/journal-home.tcl?issn=00346535

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Cited by:
  1. Sokbae 'Simon' Lee & Yoon-Jae Whang, 2009. "Nonparametric tests of conditional treatment effects," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP36/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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