Dynamic Common Correlated Effects Estimation for Panel Data R package dcce version 0.4.2 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. Software 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 Mustapha Wasseja <muswaseja@gmail.com> Comprehensive R Archive Network (CRAN) Mustapha Wasseja [aut, cre] GPL (>= 3) 2026-05-05 application/tgz https://CRAN.R-project.org/package=dcce doi:10.32614/CRAN.package.dcce