First check the level of stationarity of your variables. If some of your variables are integrated of order one and others are at zero, you simply use PARDL. Some economists also run PADRL even when all the variables are I(1).

I recommend running dynamin panel ARDL models (MG, PMG, and DFE) for better empirical analysis, if your model does not have the endogeneity issue. Because these models can be run even if your variables are I(0).

You can refer to the following article.

Article An Autoregressive Distributed Lag Modeling Approach to Co-in...

Thanks for the question,

The Panel ARDL model can be used when the variables are integrated of both I(0) and I(1) but not I(2)

When we have variables mix of I(0) and I(1) and same sample size atleast 30 observations.

Thank you all for your inputs

I do appreciate then all

The Augmented Dickey-Fuller (ADF) test and the concept of autoregressive distributed lag (ADRL) models are commonly used in time series econometrics to examine the presence of unit roots in individual time series data.

However, when it comes to panel data analysis, a similar concept known as the Panel Unit Root Test is employed to test for unit roots in a panel dataset.

Let's clarify the distinction between these two approaches:

The most commonly used panel unit root tests include:

• Levin-Lin-Chu (LLC) Test: This test extends the ADF test to panel data by considering both cross-sectional and time series dimensions. It examines whether a unit root is present in individual time series within the panel.
• Im-Pesaran-Shin (IPS) Test: Similar to the LLC test, the IPS test is a panel unit root test that considers the presence of unit roots in individual time series within the panel. It accounts for cross-sectional dependence and heterogeneity.
• Maddala-Wu Test: This test allows for different orders of integration (I(0), I(1), I(2), etc.) within the panel data and is suitable for situations where some series may be stationary while others are non-stationary.

The decision to use a panel unit root test like LLC, IPS, or Maddala-Wu depends on the characteristics of your panel dataset and whether you suspect unit roots in the individual time series.

These tests are essential for understanding whether differencing or other transformations are needed to make the data stationary before proceeding with panel data analysis, such as panel cointegration tests or panel regression models.

In summary, you use panel unit root tests (e.g., LLC, IPS, Maddala-Wu) in panel data analysis to assess the stationarity of individual time series within the panel. The ADF test, on the other hand, is applied to individual time series data in isolation to investigate their stationarity properties.

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