Semiparametric causality tests using the policy propensity score
Joshua David Angrist
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Semiparametric causality tests using the policy propensity score
Published
2004
Publisher
National Bureau of Economic Research
Pages
52
Description
"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"--National Bureau of Economic Research web site.
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Frequently Asked Questions
How many pages are in Semiparametric causality tests using the policy propensity score?
This edition of Semiparametric causality tests using the policy propensity score has approximately 52 pages. Please note, this is an estimate and the exact page count can vary between hardcover, paperback, and e-book versions.
How long does it take to read Semiparametric causality tests using the policy propensity score?
For most readers, Semiparametric causality tests using the policy propensity score typically takes between 1h 5m and 43m to complete. This is based on the book's length of approximately 13,000 words and common reading speeds.
Here's a detailed breakdown: • Continuous reading at 250 WPM: approximately 52m of focused reading • Casual reading (30 minutes/day): you could finish in roughly 2 days • Estimated word count: 13,000 words
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What is the word count of Semiparametric causality tests using the policy propensity score?
The estimated word count for Semiparametric causality tests using the policy propensity score is approximately 13,000 words. This figure is calculated using industry-standard methods that consider genre-specific word density patterns, typical formatting and layout characteristics, and standard words-per-page ratios for published books.
This is an approximation — actual word count may vary based on font size, formatting, edition, and the presence of illustrations or charts.
Who is the author of Semiparametric causality tests using the policy propensity score?
Semiparametric causality tests using the policy propensity score was written by Joshua David Angrist.
When was Semiparametric causality tests using the policy propensity score published?
The publication date for this specific edition is 2004. The original work may have been published on a different date.