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Semiparametric Causality Tests Using the Policy Propensity Score

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  • Joshua D. Angrist
  • Guido M. Kuersteiner

Abstract

Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a semi-parametric test for causality in models linking a binary treatment or policy variable with unobserved potential outcomes. The procedure is semiparametric in the sense that we model the process determining treatment -- the policy propensity score -- but leave the model for outcomes unspecified. This general approach is motivated by the notion that we typically have better prior information about the policy determination process than about the macro-economy. A conceptual innovation is that we adapt the cross-sectional potential outcomes framework to a time series setting. This leads to a generalized definition of Sims (1980) causality. We also develop a test for full conditional independence, in contrast with the usual focus on mean independence. Our approach is illustrated using data from the Romer and Romer (1989) study of the relationship between the Federal reserve's monetary policy and output.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 10975.

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Date of creation: Dec 2004
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Handle: RePEc:nbr:nberwo:10975

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  1. Romer, Christina D. & Romer, David H., 1997. "Identification and the narrative approach: A reply to Leeper," Journal of Monetary Economics, Elsevier, vol. 40(3), pages 659-665, December.
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Cited by:
  1. Sokbae Lee & Yoon-Jae Whang, 2009. "Nonparametric Tests of Conditional Treatment Effects," Cowles Foundation Discussion Papers 1740, Cowles Foundation for Research in Economics, Yale University.
  2. Song, Kyungchul, 2010. "Testing semiparametric conditional moment restrictions using conditional martingale transforms," Journal of Econometrics, Elsevier, vol. 154(1), pages 74-84, January.
  3. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design is taking the Con out of Econometrics," CEP Discussion Papers dp0976, Centre for Economic Performance, LSE.
  4. Kyungchul Song, 2007. "Testing Conditional Independence via Rosenblatt Transforms," PIER Working Paper Archive 07-026, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  5. Joeri Smits & Jeffrey S. Racine, 2013. "Testing Exclusion Restrictions in Nonseparable Triangular Models," Department of Economics Working Papers 2013-02, McMaster University.
  6. Joshua D. Angrist & Òscar Jordà & Guido Kuersteiner, 2013. "Semiparametric Estimates of Monetary Policy Effects: String Theory Revisited," NBER Working Papers 19355, National Bureau of Economic Research, Inc.
  7. Michael Lechner, 2006. "The Relation of Different Concepts of Causality in Econometrics," University of St. Gallen Department of Economics working paper series 2006 2006-15, Department of Economics, University of St. Gallen.
  8. White, Halbert, 2006. "Time-series estimation of the effects of natural experiments," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 527-566.

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