R package citation, R package reverse dependencies, R package scholars, install an r package from GitHub hy is package acceptance pending why is package undeliverable amazon why is package on hold dhl tour packages why in r package r and r package full form why is r free why r is bad which r package to install which r package has which r package which r package version which r package readxl which r package ggplot which r package fread which r package license where is package.json where is package-lock.json where is package.swift where is package explorer in eclipse where is package where is package manager unity where is package installer android where is package manager console in visual studio who r package which r package to install which r package version who is package who is package deal who is package design r and r package full form r and r package meaning what r package has what package r what is package in java what is package what is package-lock.json what is package in python what is package.json what is package installer do r package can't install r packages r can't find package r can't load package can't load xlsx package r can't install psych package r can't install sf package r Write if else in NONMEM pk pd
dcce
Dynamic Common Correlated Effects Estimation for Panel Data
View on CRAN: Click
here
Download and install dcce package within the R console
Install from CRAN:
install.packages("dcce")
Install from Github:
library("remotes")
install_github("cran/dcce") Install by package version:
library("remotes")
install_version("dcce", "0.4.2") Attach the package and use:
library("dcce")
Maintained by
Mustapha Wasseja
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-05
Latest Update: 2026-05-05
Description:
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.
How to cite:
Mustapha Wasseja (2026). dcce: Dynamic Common Correlated Effects Estimation for Panel Data. R package version 0.4.2, https://cran.r-project.org/web/packages/dcce. Accessed 21 Aug. 2026.
Previous versions and publish date:
No previous versions
Other packages that cited dcce R package
View dcce citation profile
Other R packages that dcce depends,
imports, suggests or enhances
Complete documentation for dcce
Functions, R codes and Examples using
the dcce R package
Full dcce package
functions and examples
Downloads during the last 30 days
Today's Hot Picks in Authors and Packages
preputils
Utilities for Preparation of Data Analysis
Miscellaneous small utilities are provided to mitigate issues with messy, inconsistent or high dimen ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: Josef Frank (view profile)
quickcode
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: Obinna Obianom (view profile)
artfima
ARTFIMA Model Estimation
Fit and simulate ARTFIMA. Theoretical autocovariance function and spectral density function for stat ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: A.I. McLeod (view profile)
potential
Implementation of the Potential Model
Provides functions to compute the potential model as defined by
Stewart (1941) ...
Download / Learn more Package Citations See dependency
Download / Learn more Package Citations See dependency
Maintainer: Timothée Giraud (view profile)
28,332
R Packages
239,283
Dependencies
75,034
Author Associations
28,333
Publication Badges
