Matching methods aim to improve the
validity of causal inference in observational studies by reducing
model dependence and offering intuitive diagnostics. While they
have become a part of standard tool kit for empirical researchers
across disciplines, matching methods are rarely used when analyzing
time-series cross-section (TSCS) data, which consist of a relatively
large number of repeated measurements on the same units. We develop
a methodological framework that enables the application of matching
methods to TSCS data. In the proposed approach, we first match each
treated observation with control observations from other units in
the same time period that have an identical treatment history up to
the pre-specified number of lags. We use standard matching and
weighting methods to further refine this matched set so that the
treated observation has outcome and covariate histories similar to
those of its matched control observations. Assessing the quality of
matches is done by examining covariate balance. After the
refinement, we estimate both short-term and long-term average
treatment effects using the difference-in-differences estimator,
accounting for a time trend. We also show that the proposed
matching estimator can be written as a weighted linear regression
estimator with unit and time fixed effects, providing model-based
standard errors. We illustrate the proposed methodology by
estimating the causal effects of democracy on economic growth, as
well as the impact of inter-state war on inheritance tax. The
open-source
software is available for implementing the proposed
matching methods. (Last Revised, April 2018)