WebOct 18, 2016 · xtcce is a Stata command that implements the Pesaran (2006) Common Correlated Effects estimator ('CCE') for static panel data models with strictly exogenous regressors, the Chudik and Pesaran (2015) Dynamic CCE estimator ('DCCE') for dynamic panel data models, and also the Neal (2015) 2SLS/GMM extension to account for any … WebMar 24, 2012 · Common Correlated Effects Estimation of Dynamic Panels with Cross-Sectional Dependence Authors: Gerdie Everaert Tom De Groote Request full-text Abstract We study estimation of dynamic panel...
xtdcce2 Estimating Dynamic Common Correlated Effects
WebDec 2, 2024 · Our proposed estimators include Pesaran's pooled correlated common effects (CCEP) estimator as a special case. We also show that in the presence of heterogeneous slopes our estimator is consistent under assumptions much weaker than those previously used. WebDec 26, 2014 · This paper develops new econometric methods for the estimation of high-dimensional panel data models with interactive fixed effects based on similar ideas as the very popular common correlated effects (CCE) estimator which is frequently used in the low-dimensional case. PDF View 1 excerpt, cites background ... 1 2 3 4 5 ... References snowshoe fitting
On the robustness of the pooled CCE estimator - ScienceDirect
WebThis paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variable and/or … WebJan 6, 2024 · This paper provides an approach to estimation and inference for non-linear conditional mean panel data models, in the presence of cross-sectional dependence. We modify the common correlated effects (CCE) correction of Pesaran (2006) to filter out the interactive unobserved multifactor structure. Webage is measured via an extended Common Correlated Effects (CCE) approach under a panel heterogeneous autoregression model where unobserved common factors in errors are assumed. Consistency of the CCE estimator is obtained. The common fac-tors are extracted using the principal component analysis. Empirical studies show that snowshoe feet