Data driven robust estimation methods for fixed effects panel data models
Data driven robust estimation methods for fixed effects panel data models
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DOI：
10.1080/00949655.2021.1996576
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发表时间：
2020-11
影响因子：
1.2
通讯作者：
B. H. Beyaztas;S. Bandyopadhyay
中科院分区：
文献类型：
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作者：
B. H. Beyaztas;S. Bandyopadhyay
面板数据回归模型在计量经济学、环境科学、流行病学、行为科学和社会科学等不同研究领域得到了越来越多的关注。然而，面板数据中离群观测值的存在往往会导致模型参数的有偏估计和效率低下，从而在应用最小二乘法时导致不可靠的推断。我们提出了扩展的M-估计和指数平方损失函数为基础的方法与数据驱动的调整参数的选择，以实现理想的水平的鲁棒性对离群值，而不损失估计效率。在较弱的正则性条件下，证明了估计量的相合性和渐近正态性。我们所提出的方法的有限样本性能已通过几个Monte Carlo实验和他们的结果进行了比较，从现有的方法。最后，通过对一个宏观经济数据集的分析，验证了本文方法的优越性.
The panel data regression models have gained increasing attention in different areas of research including econometrics, environmental sciences, epidemiology, behavioural and social sciences. However, the presence of outlying observations in panel data may often lead to biased and inefficient estimates of the model parameters resulting in unreliable inferences when the least squares method is applied. We propose extensions of the M-estimation and Exponential squared loss function-based approaches with a data-driven selection of tuning parameters to achieve desirable level of robustness against outliers without loss of estimation efficiency. The consistency and asymptotic normality of the proposed estimators have also been proved under some mild regularity conditions. The finite-sample performance of our proposed methods have been examined via several Monte Carlo experiments and their results are compared with the ones from existing methods. In addition, a macroeconomic dataset is analysed using the proposed methods to demonstrate their superiorities.