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Estimation in a semiparametric panel data model with nonstationarity

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posted on 2018-11-13, 17:35 authored by Chaohua Dong, Jiti Gao, Bin Peng

In this paper, we consider a partially linear panel data model with nonstationarity and certain cross-sectional dependence. Accounting for the explosive feature of the nonstationary time series, we particularly employ Hermite orthogonal functions in this study. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and the unknown functions for the cases where N and T go jointly to infinity. Rates of convergence and asymptotic normalities are established for the proposed estimators. Both the finite sample performance and the empirical applications show that the proposed estimation methods work well.

Funding

Dong thanks the support from National Natural Science Foundation of China under Grant 71671143; the authors of this paper also acknowledge the Australian Research Council Discovery Grants Program support under Grants DP150101012 and DP170104421.

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