.make_panel

Validates the data and index columns, sorts by unit then time, and attaches
<code>"unit_var"</code> and <code>"time_var"</code> attributes. Accepts either the new
<code>unit_index</code>/<code>time_index</code> arguments or the legacy <code>index</code> argument.

internal

Estimates heterogeneous coefficient models for large panels
with cross-sectional dependence. Implements the Mean Group (MG)
estimator of Pesaran and Smith (1995)
<doi:10.1016/0304-4076(94)01644-F>, the Common Correlated Effects
(CCE) and Dynamic CCE (DCCE) estimators of Pesaran (2006)
<doi:10.1111/j.1468-0262.2006.00692.x> and Chudik and Pesaran
(2015) <doi:10.1016/j.jeconom.2015.03.007>, the regularized CCE
of Juodis (2022), the Augmented Mean Group (AMG) of Eberhardt and
Teal (2010), the Interactive Fixed Effects (IFE) estimator of Bai
(2009) <doi:10.3982/ECTA6135>, and long-run estimators including
Cross-Sectionally augmented Distributed Lag (CS-DL),
Cross-Sectionally augmented Autoregressive Distributed Lag
(CS-ARDL), and Pooled Mean Group (PMG) (Chudik et al. 2016; Shin
et al. 1999). Also provides rolling-window estimation,
high-dimensional fixed effect absorption, spatial CCE via
user-supplied weight matrices, and structural break tests (Chow
and sup-Wald) following Andrews (1993), Bai and Perron (1998),
and Ditzen, Karavias and Westerlund (2024). Supplies a
comprehensive cross-sectional dependence (CD) test suite including
the Pesaran (2015) CD test <doi:10.1080/07474938.2014.956623>,
the Juodis and Reese (2022) randomized weighted CD (CDw) test,
the Baltagi et al. (2012) bias-adjusted weighted CD (CDw+) test,
the Fan et al. (2015) Power Enhancement Approach (PEA) test, and
the Pesaran and Xie (2021) bias-corrected CD (CD*) test. Further
diagnostics include the Pesaran (2007) Cross-sectionally
Augmented IPS (CIPS) panel unit root test
<doi:10.1002/jae.951>, the Westerlund (2007) panel cointegration
tests, the Dumitrescu and Hurlin (2012) panel Granger causality
test, the Im-Pesaran-Shin (IPS) and Levin-Lin-Chu (LLC) panel
unit root tests, the Pedroni (2004) and Kao (1999) residual
cointegration tests, the Swamy (1970) and Pesaran and Yamagata
(2008) slope homogeneity tests, a Hausman-type test for MG versus
pooled, the exponent of cross-sectional dependence from Bailey et
al. (2016) <doi:10.1002/jae.2490>, information criteria for
Cross-Sectional Average (CSA) selection, the rank condition
classifier, impulse response functions, cross-section and wild
bootstrap inference, and 'broom'-compatible methods.

Mustapha Wasseja

dcce

Dynamic Common Correlated Effects Estimation for Panel Data

.make_panel function

<dl><dt>data</dt>
<dd>A data.frame.</dd>
<dt>unit_index</dt>
<dd>Character scalar: name of the unit identifier column.</dd>
<dt>time_index</dt>
<dd>Character scalar: name of the time identifier column.</dd>
<dt>index</dt>
<dd>Character vector of length 2 (legacy): <code>c(unit_col, time_col)</code>. Ignored if <code>unit_index</code> and <code>time_index</code> are provided.</dd></dl>

Arguments

Prepare a panel data frame — .make_panel

<dl>

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


<dt>unit_index</dt>
<dd>Character scalar: name of the unit identifier column.</dd>


<dt>time_index</dt>
<dd>Character scalar: name of the time identifier column.</dd>


<dt>index</dt>
<dd>Character vector of length 2 (legacy): <code>c(unit_col, time_col)</code>. Ignored if <code>unit_index</code> and <code>time_index</code> are provided.</dd>

</dl>

.make_panel: Prepare a panel data frame

Description

Usage

Value

Arguments