$Ridge Regression for Panel Data Analysis$

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$Ridge Regression for Panel Data Analysis$

Hi, I am doing a unit-fixed effects panel data analysis to examine the relationship between income and education for 26 regions over 9 years. My dependent variable is Gini, independent variables are proportion of graduates from various levels of education to total population and my controls are regional GDP, regional CPI, regional unemployment, regional LFPR and net migration rate. However, my model suffers from multicollinearity. I've been looking at regularisation techniques and want to use Ridge but I am not sure if it is suitable for panel data. Can anyone give me advice/recommendations on this please? Thanks very much in advance!!

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Skalonga

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Yes, Ridge regression can be applied to panel data to help mitigate multicollinearity. When dealing with panel data, depending on the structure of your data other panel-specific techniques might be more suitable. Panel data has both a cross-sectional and time-series dimension. So, you might want to consider using Fixed Effects or Random Effects models, which are specifically designed for panel data. These methods can account for the individual-specific (e.g. in your case, region) or time-specific effects in your data.

Ridge regression is primarily used to handle multicollinearity in the cross-sectional aspect of your data, so it can be incorporated as a component within a panel data model. If you're using software like R or Python, there are libraries that support panel data modeling with regularization techniques. For instance, in Python, you can use libraries like statsmodels or linearmodels to perform panel data analysis with regularization. In R, you can use packages like plm or lmridge.

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$Ridge Regression for Panel Data Analysis$

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