Abstract
In this paper, we consider a semiparametric single index panel data model with cross-sectional dependence, high-dimensionality and stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and a link function for the case where both N and T go to infinity. Rates of convergence and asymptotic normality consistencies are established for the proposed estimates. Our experience suggests that the proposed estimation method is simple and thus attractive for finite-sample studies and empirical implementations. Moreover, both the finite-sample performance and the empirical applications show that the proposed estimation method works well when the cross-sectional dependence exists in the data set.
Highlights
Single-index models have been studied by both econometricians and statisticians in the past twenty years
The estimation techniques proposed in this paper can be extended to the multi–factor structure model. (Under certain restrictions similar to those of Su and Jin (2012), a semiparametric single–index extension can be achieved.) we add fixed effects to the model and do not impose any particular assumptions on them, so they can be correlated with the regressors to capture unobservable heterogeneity
We do not impose any assumption on the fixed effects in this paper, so they can be correlated with the regressors to capture unobservable heterogeneity
Summary
Single-index models have been studied by both econometricians and statisticians in the past twenty years. We aim at establishing consistent closed–form estimates for a semiparametric single-index panel data model with both cross-sectional dependence and stationarity for the case where both N and T go to ∞. It proposes a semiparametric single–index panel data model to simultaneously accommodate cross–sectional dependence, high–dimensionality, stationarity and unobservable heterogeneity; 2 It establishes simple and consistent closed–form estimates for both unknown parameters and link function, and the closed–form estimates are easy to implement in practice; 3. It establishes both rates of convergence and asymptotic normality results for the estimates under a general spatial error dependence structure; and.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
