.build_unit_X

Build the regressor matrix for a single unit

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

.build_unit_X function

<dl><dt>panel</dt>
<dd>Panel data.frame.</dd>
<dt>idx</dt>
<dd>Integer indices for this unit.</dd>
<dt>x_names</dt>
<dd>Character: structural regressor names.</dd>
<dt>csa_colnames</dt>
<dd>Character: CSA column names (or NULL).</dd>
<dt>include_constant</dt>
<dd>Logical: include intercept?</dd>
<dt>unit_trend</dt>
<dd>Logical: include time trend?</dd></dl>

Arguments

Build the regressor matrix for a single unit — .build_unit_X

<dl>

<dt>panel</dt>
<dd>Panel data.frame.</dd>


<dt>idx</dt>
<dd>Integer indices for this unit.</dd>


<dt>x_names</dt>
<dd>Character: structural regressor names.</dd>


<dt>csa_colnames</dt>
<dd>Character: CSA column names (or NULL).</dd>


<dt>include_constant</dt>
<dd>Logical: include intercept?</dd>


<dt>unit_trend</dt>
<dd>Logical: include time trend?</dd>

</dl>

.build_unit_X: Build the regressor matrix for a single unit

Description

Usage

Value

Arguments