Basics of Panel ARDL

There are several reasons for conducting a panel data analysis. Such as: (1) the main interest is the “group” and not the individual units in the group, which means that very little information is lost by taking the panel perspective; (2) the use of panel rather than time series data not only increases the total number of observations and their variation but also reduces the noise coming from the individual time series (heteroscedasticity not an issue in panel data analysis); (3) best suited where data availability is an issue particularly for developing countries where short time spans for variables are rampant, often insufficient for fitting time series regressions; (4) there is heterogeneity (differences) among units in the panel; (5) panel estimation techniques take these heterogeneity into account by allowing for subject-specific variables; and (6) suited for studying dynamic changes due to repeated cross-sectional observations. I have listed 10 steps to panel ARDL estimations. They are: (1) specify the model; (2) describe the data; (3) perform correlation analysis; (4) perform unit root tests; (5) determine the optimal lags for the model; (6) perform the Hausman test; (7) perform cointegration test (optional); (8) estimate the model (s); (9) perform causality tests (optional) and (10) perform diagnostics (optional).
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