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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.

Suggested Citation

  • Joshua D. Angrist & Guido M. Kuersteiner, 2004. "Semiparametric Causality Tests Using the Policy Propensity Score," NBER Working Papers 10975, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:10975 Note: ME
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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. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    2. Òscar Jordà & Alan M. Taylor, 2016. "The Time for Austerity: Estimating the Average Treatment Effect of Fiscal Policy," Economic Journal, Royal Economic Society, vol. 126(590), pages 219-255, February.
    3. Mauricio Villamizar, 2014. "Identifying the Effects of Simultaneous Monetary Policy Shocks. Fear of Floating under Inflation targeting," Borradores de Economia 835, Banco de la Republica de Colombia.
    4. Song, Kyungchul, 2010. "Testing semiparametric conditional moment restrictions using conditional martingale transforms," Journal of Econometrics, Elsevier, vol. 154(1), pages 74-84, January.
    5. Joeri Smits & Jeffrey S. Racine, 2013. "Testing Exclusion Restrictions in Nonseparable Triangular Models," Department of Economics Working Papers 2013-02, McMaster University.
    6. Sokbae Lee & Yoon-Jae Whang, 2009. "Nonparametric tests of conditional treatment effects," CeMMAP working papers CWP36/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. 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.
    8. 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.
    9. White, Halbert, 2006. "Time-series estimation of the effects of natural experiments," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 527-566.
    10. Mauricio Villamizar-Villegas, 2016. "Identifying The Effects Of Simultaneous Monetary Policy Shocks," Contemporary Economic Policy, Western Economic Association International, vol. 34(2), pages 268-296, April.
    11. 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.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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