Missing values in panel data unit root tests

Yiannis Karavias*, Elias Tzavalis, Haotian Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

57 Downloads (Pure)


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.

Original languageEnglish
Article number12
Number of pages11
Issue number1
Publication statusPublished - 16 Mar 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • bias correction
  • local power function
  • missing values
  • panel unit root tests
  • structural breaks
  • unbalanced panel

ASJC Scopus subject areas

  • Economics and Econometrics


Dive into the research topics of 'Missing values in panel data unit root tests'. Together they form a unique fingerprint.

Cite this