Common Correlated Effects Estimation of Dynamic Panels with Cross-Sectional Dependence

Everaert, Gerdie; Groote, Tom De

doi:10.1080/07474938.2014.966635

Cited by 89 publications

(55 citation statements)

References 20 publications

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“…Sarafidis and Wansbeek (2012) present evidence that estimators neglecting cross-sectional dependence across errors are inefficient and their estimated standard errors are biased and inconsistent. Everaert and De Groote (2016) show that the bias due to cross-sectional dependence is positive for large values of T, as it is in our case, and that the bias does not decrease as T grows. Notes: Estimations are based on the dynCCEMG 1 , Chudik and Pesaran's (2015a) dynamic common correlated effects mean group estimator (augmented with a reduced number of the first lag of the cross-section averages).…”

Section: A Robustness Checkmentioning

confidence: 50%

Regional and Sectoral Evidence of the Macroeconomic Effects of Labor Reallocation: A Panel Data Analysis

2016

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This article revisits the sectoral shifts hypothesis by examining unemployment fluctuations for 48 U.S. states over the period 1990:M01-2011:M12. We develop a panel approach that incorporates dynamics, parameter heterogeneity, aggregate factors, and cross-sectional dependence (CSD). Our findings provide support for a positive and significant effect of the employment dispersion index on unemployment. This outcome is robust under alternative specifications and measures of employment dispersion. The empirical evidence corroborates the presence and relevance of CSD and heterogeneity among states. The results show that, once unobserved common factors and cross-state heterogeneity are taken into account, labor reallocation has a significant effect on unemployment that is half the size of the estimate when cross-sectional dependence is not taken into account. (JEL E24, E32, J21, R23, C23)

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Section: A Robustness Checkmentioning

confidence: 50%

Regional and Sectoral Evidence of the Macroeconomic Effects of Labor Reallocation: A Panel Data Analysis

2016

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“…As Everaert and De Groot (2015) point out, the recent panel data literature has shifted its attention from the focus on the consistent specification of the models' temporal dynamics to the account of cross-sectional or spatial dependence. In fact, taking regions as isolated islands (equation 3) is an over-simplistic assumption (Acemoglu 2009).…”

Section: Convergence Transitory Dynamics and Spatial Links In Empirimentioning

confidence: 99%

EU Structural Funds and Regional Income Convergence A Sobering Experience

2016

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“…This requirement is due to the estimation method employed in Step 1, rather than to the inference on factors and loadings per se. Indeed, the CCE is known not to work in presence of weakly exogenous regressors (see Everaert and Groote, 2012;and Chudik and Pesaran, 2013). However, the assumption of strict exogeneity can be readily relaxed (accommodating e.g.…”

Section: Discussionmentioning

confidence: 99%

Inference on factor structures in heterogeneous panels

Castagnetti

Rossi

Trapani

2015

Journal of Econometrics

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This paper develops an estimation and testing framework for a stationary large panel model with observable regressors and unobservable common factors. We allow for slope heterogeneity and for correlation between the common factors and the regressors. We propose a two stage estimation procedure for the unobservable common factors and their loadings, based on Common Correlated Effects estimator and the Principal Component estimator. We also develop two tests for the null of no factor structure: one for the null that loadings are cross sectionally homogeneous, and one for the null that common factors are homogeneous over time. Our tests are based on using extremes of the estimated loadings and common factors. The test statistics have an asymptotic Gumbel distribution under the null, and have power versus alternatives where only one loading or common factor differs from the others. Monte Carlo evidence shows that the tests have the correct size and good power.JEL codes: C12, C33.

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Common Correlated Effects Estimation of Dynamic Panels with Cross-Sectional Dependence

Cited by 89 publications

References 20 publications

Regional and Sectoral Evidence of the Macroeconomic Effects of Labor Reallocation: A Panel Data Analysis

Regional and Sectoral Evidence of the Macroeconomic Effects of Labor Reallocation: A Panel Data Analysis

EU Structural Funds and Regional Income Convergence A Sobering Experience

Inference on factor structures in heterogeneous panels

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