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)