Matching methods improve the validity of
causal inference by reducing model dependence and offering intuitive
diagnostics. While they have become a part of the standard tool kit
across disciplines, matching methods are rarely used when analyzing
time-series cross-sectional data. We fill this methodological gap.
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 and matched
control observations have similar covariate values. Assessing the
quality of matches is done by examining covariate balance. Finally,
we estimate both short-term and long-term average treatment effects
using the difference-in-differences estimator, accounting for a time
trend. We illustrate the proposed methodology through simulation
and empirical studies. An
open-source software
package is available for implementing the proposed
matching methods. (Last Revised, January 2020)