Thus, graphically, it allows the level and slope of your panels to vary on the y axis. Random effects doesn't make this assumption, namely, that there's no/ negligible unobserved confounding. Generally speaking, unless you're doing some experiment, 9 times out of 10, using FE will make sense, but the Hausman test can assist in arbitration this. Thus, graphically, it forces all panels to have the same slope of of the intercepts, at different points along the y axis (if I recall correctly). As my old methods teacher once said, in his thick Kentucky accent, "There's something that makes Alabam-er, Alabam-er". FE (ostensibly) soaks up any time invariant and observed effects in your given units. The more that I try to read about panel data models, the more I get myself confused so if someone could explain my specific case that would be amazing. I'm sorry these are quite a few questions but any help at all would be GREATLY appreciated. I understand that I am not to include variables that do not vary over time within a country- or is that only for fixed effects? So I'm not sure if my x variables are ' correct'
My other x variables are population density, share of elderly, total tax revenue, GDP per capita and Gini. I am running a regression of total inflow migration on gov spending on healthcare per capita. Should I go ahead with FE or follow what is recommended? I have seen that a command, sigmamore can also be added to the test as it is less likely to produce a non–positive-definite-differenced covariance matrix, but how do I check if I need this?Īlso, does RE model being the best-suited model mean I simply use the ,re output as my result or does it mean there's something in my variables and controls that needs to be changed or considered? However, I strongly doubt that my individual effects are uncorrelated with my x variables. Is it the case that you can have a fixedeffect estimatorin a randomeffects modeland if you do that your estimate is not consistent?įor context, I am running a panel data analysis in STATA and after conducting a Hausman Test I obtain Prob > chi2 = 0.4504, indicating I should use a random effects model. Hello! I am trying to understand the different Panel Data models and I am getting confused by the different terms used, i.e., Random effect models and Random effects estimators and Fixed effect models and Fixed effect estimators- are these 4 all different?