Semiparametric Causality Tests Using the Policy Propensity Score
54 Pages Posted: 15 Dec 2004 Last revised: 29 Dec 2022
Date Written: December 2004
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: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
Does Monetary Policy Matter? a New Test in the Spirit of Friedman and Schwartz
By Christina D. Romer and David H. Romer
-
Monetarist Interpretations of the Great Depression: An Evaluation and Critique
By Robert J. Gordon and James A. Wilcox
-
A Tax-Based Test for Nominal Rigidities
By James M. Poterba, Julio J. Rotemberg, ...
-
Was the Disinflation of the Early 1980s Anticipated?
By Jed Devaro and Michael Dotsey
-
Choosing the Federal Reserve Chair: Lessons from History
By Christina D. Romer and David H. Romer