Talk:Fixed effects model

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Fixed effects model should be merged into this article, and the seemingly opposite descriptions on that page should either be harmonized or deleted if they simply represent an error. Torfason 14:39, 18 April 2007 (UTC)[reply]

YES. Jeremy Tobacman 10:48, 8 August 2007 (UTC)[reply]

I'm going ahead. Jeremy Tobacman 10:51, 8 August 2007 (UTC)[reply]

The article doesn't seem to be correct when it says "A random effects model makes the additional assumption that the individual effects are randomly distributed. It is thus not the opposite of a fixed effects model, but a special case." My understanding and what I have read elsewhere is that the random effects model is more general than the fixed effects model. Setting the variance of the effect to zero derandomizes the random effects and makes them fixed effects. I didn't change the article yet because I'm not familiar with the formalisms of this area yet. Thoughts? --Tekhnofiend (talk) 23:17, 3 March 2008 (UTC)[reply]

Add example of the shortcoming. E.g. cannot estimate Race, etc.

Add the matrix version of the estimator cancan101 (talk) 00:18, 20 February 2009 (UTC)[reply]

The Fixed and Random effect assumptions as stated were clearly wrong. The RE assumption is that the individual specific effect is uncorrelated with the regressors, not that it is just random. The difference is that if it is uncorrelated it can be added to the error of the model and estimated normally. The FE assumption is that the random effect is actually correlated with the regressors so that if you just added it to the error of the model there will be a problem with endogeneity. I also added the LD and FD estimators, a discussion about dummy variables, the hausman-taylor method, and the hausman test for testing RE vs. RE. The section about using dummy variables to estimate a fixed effect model should be expanded and there should be a section about correlated random effects. Mikethechampion (talk) 02:10, 5 May 2009 (UTC)[reply]

Needs Work/Contradicts?

I'm new to editing, so I want to suggest a two changes before making them. If there's no objection after a while, I'll edit the article.

First, I think it's important to emphasize the importance of categorical explanatory variables in this context. By traditional definitions, continuous explanatory variables are fixed effects, so it is only important to consider categorical variables and their interactions when deciding between a fixed and random/mixed effects model. When we treat a categorical explanatory variable as a fixed effect, we assume that we have observed every category of interest. When we treat it as a random effect, we assume that the categories follow a categorical distribution and we have only observed a small sample of all possible categories.
Second, the article uses the panel-data terminology subject-specific effects to refer to effects impacting groups of observations with the same value of a categorical explanatory variable. If multiple measurements are made on one subject it is correct to call this a subject-specific effect, but outside of panel/longitudinal data that's not generally true. In the example of students' test scores given in Random effects model, each observation comes from a different individual student, and the effect of the school on the student's score is more accurately described as school-specific or group-specific, not subject-specific. --Maximillion Likelihood (talk) 00:02, 3 December 2014 (UTC)[reply]

This article seems to contradict several others related to it (random effects and ANOVA). If it doesn't, it's written poorly enough that it appears to. I deleted some of the random stuff about race (what?), but don't have the competence to attack the rest of the qualitative description. Help? Executive Outcomes (talk) 14:33, 21 July 2009 (UTC)[reply]

Also, the link "Distinguishing Between Random and Fixed: Variables, Effects, and Coefficients" now links to some university home page with no relation to this article Executive Outcomes (talk) 14:37, 21 July 2009 (UTC)[reply]

The notations $\perp$ and $\not \perp$ need some explanation or at least a link to an explanation. Melcombe (talk) 14:31, 18 September 2009 (UTC)[reply]

Cleaned up the confusing notation. I think the apparent contradiction is because this article is written using econometrics jargon and the RE and ANOVA articles use statistics jargon. Someone needs to standardize these articles and connect all the fixed effects and random effects links. Perhaps this article should be titled, Fixed Effects Model so that it is symmetric with Random Effect Model. Mikethechampion (talk) 06:03, 5 November 2009 (UTC)[reply]

I have had the article renamed as suggested, and have rewritten the start of the lead correspondingly. Also I have removed the external link mentioned above as irrelevant. Melcombe (talk) 15:30, 9 November 2009 (UTC)[reply]

Some example tables?

When learning about various regression techniques, I've had the most luck understanding things when an example table has been given. If there is a way to run a fixed effects regression, and present the results in a form similar to the output of a popular statistics tool, I think this would be incredibly helpful in understanding what fixed effects regressions are measuring. —Preceding unsigned comment added by 134.10.12.33 (talk) 21:09, 19 April 2010 (UTC)[reply]

I agree, an example would be very useful. See for example http://www.nyu.edu/its/pubs/connect/fall03/yaffee_primer.html. Even an example equation as in Random_effects_model#Simple_example would be good. dfrankow (talk) 19:20, 4 March 2011 (UTC)[reply]