Panel Unit Root Test

Panel unit root tests, such as the Levin-Lin-Chu, Im-Pesaran-Shin, and Fisher-type tests, check whether a variable observed across many units over time is nonstationary by pooling information across the panel.

패널 단위근 검정은 Levin-Lin-Chu, Im-Pesaran-Shin, Fisher형 검정처럼 여러 개체의 시계열 정보를 결합하여 패널 변수가 비정상인지를 검정하는 방법입니다.

How do I test whether variables in my panel data are stationary before running a panel time-series model?

패널 시계열 모형을 돌리기 전에 패널 변수들이 정상성을 갖는지 어떻게 검정하나요?

Panel unit root tests assess whether a variable measured for many units over time contains a unit root, gaining power over single-series tests by combining information across units. They are a standard first step before panel cointegration or dynamic panel models, and second-generation tests are needed when units are cross-sectionally dependent.

When to use Panel Unit Root Test

• Country, region, or firm panels with a moderately long time dimension
• Checking stationarity before panel cointegration or panel VAR models
• Deciding whether to model variables in levels or first differences

When to use something else

• A single time series: use the augmented Dickey-Fuller test
• Strong cross-sectional dependence among units: use a second-generation test such as Pesaran's CIPS

Assumptions and requirements

• Time dimension long enough for time-series inference
• Cross-sectional independence for first-generation tests
• Lag length and deterministic terms (constant, trend) chosen appropriately
• Balanced panel required by some tests

What the analysis reports

• Test statistic and p-value for each test
• Null and alternative hypotheses of each test
• Selected lag lengths
• Results in levels and in first differences

How to report it

The Im-Pesaran-Shin test rejected the null hypothesis that all panels contain a unit root for [variable] in first differences, W-t-bar = [value], p = [value].

Related methods

• Augmented Dickey-Fuller Test
• Panel Cointegration Test
• Panel VAR

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