文章基本信息

标题：Missing Values in Panel Data Unit Root Tests
本地全文：下载
作者：Yiannis Karavias ; Elias Tzavalis ; Haotian Zhang 等
期刊名称：Econometrics
印刷版ISSN：2225-1146
出版年度：2022
卷号：10
期号：1
页码：12
DOI：10.3390/econometrics10010012
语种：English
出版社：MDPI, Open Access Journal
摘要：Missing data or missing values are a common phenomenon in applied panel data research and of great interest for panel data unit root testing. The standard approach in the literature is to balance the panel by removing units and/or trimming a common time period for all units. However, this approach can be costly in terms of lost information. Instead, existing panel unit root tests could be extended to the case of unbalanced panels, but this is often difficult because the missing observations affect the bias correction which is usually involved. This paper contributes to the literature in two ways; it extends two popular panel unit root tests to allow for missing values, and secondly, it employs asymptotic local power functions to analytically study the impact of various missing-value methods on power. We find that zeroing-out the missing observations is the method that results in the greater test power, and that this result holds for all deterministic component specifications, such as intercepts, trends and structural breaks.