Im working on panel data and when I performed fixed effects model I get high r square and less significant variables. All the underlying assumptions of regression are satisfied. Kindly let me know is dis a problem with respect to high r square and less significant variables?
That is what I would expect to happen - you are in effect putting in a dummy for each and every higher unit that is being repeatedly measured and your R-squared will therefore go up, but the effect of your other predictor variables is likely to diminish as the time-invariant element of their effect is controlled.
That is what I would expect to happen - you are in effect putting in a dummy for each and every higher unit that is being repeatedly measured and your R-squared will therefore go up, but the effect of your other predictor variables is likely to diminish as the time-invariant element of their effect is controlled.
Hi Irem,
As mentioned by Kelvyn, including a dummy variable for time-specific effects may have contributed to your problem. Have you performed the Hausman test or a Wald test to determine whether fixed or random effects model should be used? To include the effects of a time-invariant variable, a random-effects model would be better.
Dear Hemlata
I performed hausman test and the result shows fixed effect model is appropriate. Do I need to fix the issue?
I do not want to confuse and muddy the water but a Hausmann test is not really to distinguish between random and fixed effects analysis , it is telling you that there is an important cross-sectional effect . For me, it is possible to model the within and between relation simultaneously using random effects. my views on this are challenged|!
See
https://en.wikipedia.org/wiki/Kelvyn_Jones
section beginning " He (with colleagues) has challenged the 'gold standard' that fixed effects should be the standard approach to the analysis of Panel data and that a Hausman test is an appropriate way of choosing between a Fixed effects model and a Random effects model. " for pointers to the debate
That being said one way is to use fixed effects as you have correctly done and get rid of the time-invariant effect but this may be removing substantively interesting effects (baby being removed by the bathwater). If you specify the within and between model random effects model you will get exactly the same results as fixed effects for the within relationship but additionally information on the between effect.
Article Explaining Fixed Effects: Random Effects Modeling of Time-Se...
Yeah, it is true that the Hausman test is not the gold standard to confirm whether a fixed or random effects model should be used. Model choice depends on type of data (multi-level or otherwise) & the characteristics like relationship being modelled. Besides other limitations exist like the Hausman test is only valid under homoscedasticity. I just wanted to ascertain why you chose the fixed effects model.
Regarding mixed effects model or modelling the within and between relation simultaneously using random effects, I think Kelvyn would be in a better position to explain as I am not an expert on this.
The FE and RE model are suitable if your variable are all I(0). Be aware of spurious correlation. Might be some multicolinearity effects due to the fact that two or more variable are directly correlated with time @trend.
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