A Varying-Coefficient Panel Data Model with Fixed Effects: Theory and an Application to U.S. Commercial Banks
Guohua Feng (),
Jiti Gao (),
Bin Peng () and
Xiaohui Zhang ()
No 9/15, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
In this paper, we propose a panel data semiparametric varying-coefficient 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. Finally, we illustrate our model by analysing the effects of state-level banking regulations on the returns to scale of commercial banks in the U.S. Our empirical results suggest that returns to scale is higher in more regulated states than in less regulated states.
Keywords: Categorial variable; estimation theory; nonlinear panel data model; returns to scale. (search for similar items in EconPapers)
JEL-codes: C23 C51 D24 G21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban, nep-ecm and nep-eff
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Journal Article: A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks (2017)
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