Missing Values in Panel Data Unit Root Tests

Yiannis Karavias, Elias Tzavalis and Haotian Zhang
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Haotian Zhang: Department of Economics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China

Econometrics, 2022, vol. 10, issue 1, 1-11

Abstract: 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.

Keywords: panel unit root tests; local power function; missing values; bias correction; unbalanced panel; structural breaks (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2022
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