A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks

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• 作者：Guohua Feng^a ; ^{Guohua.Feng@unt.edu" class="auth_mail" title="E-mail the corresponding author} ; Jiti Gao^b ; ^{Jiti.Gao@monash.edu" class="auth_mail" title="E-mail the corresponding author} ; Bin Peng^c ; ^{Bin.Peng@uts.edu.au" class="auth_mail" title="E-mail the corresponding author} ; Xiaohui Zhang^d ; ^{x.zhang1@exeter.ac.uk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：C23 ; C51 ; D24 ; G21
• 刊名：Journal of Econometrics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：196
• 期：1
• 页码：68-82
• 全文大小：607 K

文摘

In this paper, we propose a semiparametric varying-coefficient categorical panel data model in which covariates (variables affecting the coefficients) are purely categorical. This model has two features: first, fixed effects are included to allow for correlation between individual unobserved heterogeneity and the regressors; second, it allows for cross-sectional dependence through a general spatial error dependence structure. We derive a semiparametric estimator for our model by using a modified within transformation, and then show the asymptotic and finite properties for this estimator under large data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304407616301786&_mathId=si80.gif&_user=111111111&_pii=S0304407616301786&_rdoc=1&_issn=03044076&md5=345d3132899cc91adba1ed51c1bd4384" title="Click to view the MathML source">N$N$ and data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304407616301786&_mathId=si81.gif&_user=111111111&_pii=S0304407616301786&_rdoc=1&_issn=03044076&md5=09854b2aafd10a4c93bce7f2d5bf1cab" title="Click to view the MathML source">T$T$. The Monte Carlo study shows that our methodology works well for both large data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304407616301786&_mathId=si80.gif&_user=111111111&_pii=S0304407616301786&_rdoc=1&_issn=03044076&md5=345d3132899cc91adba1ed51c1bd4384" title="Click to view the MathML source">N$N$ and data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304407616301786&_mathId=si81.gif&_user=111111111&_pii=S0304407616301786&_rdoc=1&_issn=03044076&md5=09854b2aafd10a4c93bce7f2d5bf1cab" title="Click to view the MathML source">T$T$, and large data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304407616301786&_mathId=si80.gif&_user=111111111&_pii=S0304407616301786&_rdoc=1&_issn=03044076&md5=345d3132899cc91adba1ed51c1bd4384" title="Click to view the MathML source">N$N$ and small data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0304407616301786&_mathId=si81.gif&_user=111111111&_pii=S0304407616301786&_rdoc=1&_issn=03044076&md5=09854b2aafd10a4c93bce7f2d5bf1cab" title="Click to view the MathML source">T$T$ cases. Finally, we illustrate our model by analyzing the effects of state-level banking regulations on the returns to scale of commercial banks in the US. Our empirical results suggest that returns to scale is higher in more regulated states than in less regulated states.