[gov3009-l] Applied Stats - 2/17 - Jann Spiess

Aaron Kaufman aaronkaufman at fas.harvard.edu
Mon Feb 15 09:16:47 EST 2016


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Jann
Spiess*, PhD Candidate in Economics at Harvard University. He will be
presenting work joint work with Alberto Abadie entitled *Robust
Post-Matching Inference**.*  Please find the abstract below and on the
website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/2172016-jann-spiess-harvard-title-coming-soon>
.

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.

-- Aaron Kaufman

Title: Robust Post-Matching Inference

Nearest-neighbor matching (Cochran, 1953; Rubin, 1973) is a popular
nonparametric tool to create balance between treatment and control groups
in non-experimental data. As a preprocessing step for regression analysis,
it reduces the dependence on parametric modeling assumptions (Ho et al.,
2007). In this paper, we show how to obtain valid standard error estimates
for linear regression after nearest-neighbor matching without replacement.
We show that standard error estimates that ignore the matching step are not
generally valid if the regression model is misspecified, and can lead to
severe over- or underestimation of the asymptotic variance of the
post-matching estimator. As a remedy, we offer two easily implementable
tools for inference that are robust to misspecification: First, we show
that standard errors clustered at the match level are valid. Second, we
show that a simple nonparametric block bootstrap procedure yields a valid
approximation of the distribution of the post-matching estimator. A
simulation study and an empirical example demonstrate the importance and
usefulness of our results.
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