I have panel data (T=10, N=26) where all variables are integrated I(1) with cross-section dependence. I applied Westerlund test and found no cointegration. So I proceeded with Pvar (Panel var) estimation. However, I want to confirm the robustness of my analysis by applying another estimation technique. Any advice?
It depends on the nature of your research problem. If it is not necessarily a question of analyzing the transmission mechanisms between variables using a VAR model, but also of examining the short and long run effects of a set of regressors on a target variable. Perhaps in this case you could fit a panel ARDL model, with the Pooled Mean Group (Pesaran et al., 1999) as estimation method, in order to capture the heterogeneity between individuals in the panel.
Dear Tara Vanlı ,
If the PVAR estimation technique and associated results address/meet the research objectives of your study, another post estimation test you can perform to check the robustness of your results is the "stability condition" of panel VAR estimates by calculating the modulus of each eigenvalue of the fitted model (eg. if you are using Stata software, the syntax is "pvarstable" - applied to active estimation results.
A VAR model is deemed stable if all moduli of the companion matrix are strictly less than one (Lutkepohl, 2005; and Hamilton, 1994), where stability implies that the panel VAR is invertible and has an infinite-order vector moving average (VMA) representation which provides known interpretation to estimated IRFs and FEVDs.
Panel data analysis is a statistical method used to analyze two-dimensional panel data. Panel data is a collection of observations for multiple subjects at different time intervals. Panel data analysis is used in many fields, including economics, finance, and social science.
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