check_time_data

Time data should be consecutive integers: When it is not, try to convert it as best we can or throw an error. If function does not fail, returns the data as data frame object, either processed or not as appropriately

internal

Implements a set of methodological tools
that enable researchers to apply matching methods to
time-series cross-sectional data. Imai, Kim, and Wang
(2023) <http://web.mit.edu/insong/www/pdf/tscs.pdf>
proposes a nonparametric generalization of the
difference-in-differences estimator, which does not rely
on the linearity assumption as often done in
practice. Researchers first select a method of matching
each treated observation for a given unit in a
particular time period with control observations from
other units in the same time period that have a similar
treatment and covariate history. These methods include
standard matching methods based on propensity score and
Mahalanobis distance, as well as weighting methods. Once
matching and refinement is done,
treatment effects can be estimated with
standard errors. The package also offers diagnostics for researchers to assess the quality
of their results.

In Kim

PanelMatch

Matching Methods for Causal Inference with Time-Series
Cross-Sectional Data

In Song Kim

Adam Rauh

Erik Wang

Kosuke Imai

check_time_data function

<dl><dt>data</dt>
<dd>data.frame object.</dd>
<dt>time.id</dt>
<dd>string specifying the time id variable.</dd></dl>

Arguments

check_time_data — check_time_data

<dl>

<dt>data</dt>
<dd>data.frame object.</dd>


<dt>time.id</dt>
<dd>string specifying the time id variable.</dd>

</dl>

check_time_data: check_time_data

Description

Usage

Value

Arguments

Details