dcce: Dynamic Common Correlated Effects Estimation for Panel Data

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.

Version:	0.4.2
Depends:	R (≥ 4.1.0)
Imports:	stats, Matrix, collapse (≥ 2.0.0), sandwich, generics, rlang (≥ 1.1.0), cli (≥ 3.0.0), tibble, Rcpp (≥ 1.0.0)
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	broom, ggplot2, lifecycle, plm, testthat (≥ 3.0.0), knitr, rmarkdown, marginaleffects, parallel
Published:	2026-05-05
DOI:	10.32614/CRAN.package.dcce
Author:	Mustapha Wasseja [aut, cre]
Maintainer:	Mustapha Wasseja <muswaseja at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	dcce results

Documentation:

Reference manual:	dcce.html , dcce.pdf
Vignettes:	Introduction to the dcce Package: DCCE Estimation for Panel Data (source, R code)

Downloads:

Package source:	dcce_0.4.2.tar.gz
Windows binaries:	r-devel: dcce_0.4.2.zip, r-release: dcce_0.4.2.zip, r-oldrel: dcce_0.4.2.zip
macOS binaries:	r-release (arm64): dcce_0.4.2.tgz, r-oldrel (arm64): dcce_0.4.2.tgz, r-release (x86_64): dcce_0.4.2.tgz, r-oldrel (x86_64): dcce_0.4.2.tgz

Linking:

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