当前位置: X-MOL 学术Stat. Probab. Lett. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Weighted least squares estimation in a binary random coefficient panel model with infinite variance
Statistics & Probability Letters ( IF 0.8 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.spl.2020.108932
Eunju Hwang

Abstract This article investigates the asymptotic properties of weighted least squares estimators (WLSE) for a binary random coefficient autoregressive (RCA) panel model with heterogeneous variances of panel variables. It is an extension of Johansen and Lange (2013) to a panel model, which is more practical for macroeconomic time series data. We develop asymptotic properties of the WLSE in cases of finite and infinite variances, respectively, as both sizes of panels and samples tend to infinity. In the latter case with infinite variance, the asymptotic for the WLSE β ˆ of the coefficient β is shown to be a curious result β ˆ ⟶ p β − 1 . It is proven by using the notion of a tail index and the stable distribution limit. In a Monte Carlo simulation, feasible WLSEs are computed iteratively and some evidences are given to verify our theoretical results.

中文翻译:

具有无限方差的二元随机系数面板模型中的加权最小二乘估计

摘要 本文研究了具有面板变量异质方差的二元随机系数自回归 (RCA) 面板模型的加权最小二乘估计量 (WLSE) 的渐近特性。它是Johansen and Lange (2013) 对面板模型的扩展,对于宏观经济时间序列数据更实用。我们分别在有限和无限方差的情况下开发 WLSE 的渐近特性,因为面板和样本的大小都趋于无穷大。在后一种具有无限方差的情况下,系数 β 的 WLSE β ˆ 的渐近被证明是一个奇怪的结果 β ˆ ⟶ p β − 1 。它是通过使用尾指数和稳定分布极限的概念来证明的。在蒙特卡罗模拟中,
更新日期:2021-01-01
down
wechat
bug