Long-Run Effects in Large Heterogenous Panel Data Models with Cross-Sectionally Correlated Errors
Long-Run Effects in Large Heterogenous Panel Data Models with Cross-Sectionally Correlated Errors
复制标题
具有横截面相关误差的大型异质面板数据模型的长期效应
DOI：
10.24149/gwp223
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
M. Raissi
中科院分区：
文献类型：
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作者：
A. Chudik;Kamiar Mohaddes;M. Pesaran;M. Raissi
本文开发了一种横截面增强分布式滞后（CS-DL）方法，用于估计具有横截面相关误差的大型动态异构面板数据模型的长期效应。在时间维度（T）和截面维度（N）都很大的情况下，在系数异质性下推导了CS-DL估计量的渐近分布。将 CS-DL 方法与基于自回归分布滞后 (ARDL) 规范的更标准面板数据估计器进行比较。结果表明，与 ARDL 类型估计器不同，CS-DL 估计器对于动态和误差序列相关性的错误指定具有鲁棒性。理论结果通过蒙特卡洛模拟获得的小样本证据进行了说明，这表明 CS-DL 方法的性能通常优于替代面板 ARDL 估计，特别是当 T 不太大且在 30≤T 范围内时
This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T) and the crosssection dimension (N) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates particularly when T is not too large and lies in the range of 30≤T