plot.PanelData

Create basic plots of PanelData objects

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

plot.PanelData function

<dl><dt>x</dt>
<dd><code>PanelData</code> object</dd>
<dt>...</dt>
<dd>Not used</dd>
<dt>plotting.variable</dt>
<dd>character string specifying which variable to plot in the resulting figure. The values of this variable will be used to fill in cells on the resulting heatmap. Defaults to whatever is specified as the treatment variable.</dd></dl>

Arguments

Create basic plots of PanelData objects — plot.PanelData

<dl>

<dt>x</dt>
<dd><code>PanelData</code> object</dd>


<dt>...</dt>
<dd>Not used</dd>


<dt>plotting.variable</dt>
<dd>character string specifying which variable to plot in the resulting figure. The values of this variable will be used to fill in cells on the resulting heatmap. Defaults to whatever is specified as the treatment variable.</dd>

</dl>

plot.PanelData: Create basic plots of PanelData objects

Description

Usage

Value

Arguments

Examples