Quantile regression for dynamic panel data with fixed effects
Quantile regression for dynamic panel data with fixed effects
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DOI：
10.1016/j.jeconom.2011.02.016
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发表时间：
2011-09-01
影响因子：
6.3
通讯作者：
Galvao, Antonio F., Jr.
中科院分区：
文献类型：
--
作者：
Galvao, Antonio F., Jr.
本文研究了固定效应分位数回归动态面板模型。面板数据固定效应估计量在滞后因变量作为回归量时通常存在偏差。为了减少动态偏差，我们建议使用沿着滞后回归变量作为工具，并使用Zhaizhukov和汉森（2006）的工具变量分位数回归方法。此外，我们描述了如何使用估计模型进行预测。蒙特卡罗模拟表明，工具变量方法大大降低了动态偏差，预测区间的经验水平非常接近名义水平。最后，我们举例说明的程序与应用程序预测18个经合组织国家的产出增长率。(C)2011 Elsevier B.V.保留所有权利。
This paper studies a quantile regression dynamic panel model with fixed effects. Panel data fixed effects estimators are typically biased in the presence of lagged dependent variables as regressors. To reduce the dynamic bias, we suggest the use of the instrumental variables quantile regression method of Chernozhukov and Hansen (2006) along with lagged regressors as instruments. In addition, we describe how to employ the estimated models for prediction. Monte Carlo simulations show evidence that the instrumental variables approach sharply reduces the dynamic bias, and the empirical levels for prediction intervals are very close to nominal levels. Finally, we illustrate the procedures with an application to forecasting output growth rates for 18 OECD countries. (C) 2011 Elsevier B.V. All rights reserved.