In econometrics and statistics, a fixed effects model is a statistical model that represents the observed quantities in terms of explanatory variables that are treated as if the quantities were non-random. This is in contrast to random effects models and mixed models in which either all or some of the explanatory variables are treated as if they arise from random causes. Note that the biostatistics definitions differ, as biostatisticians refer to the population-average and subject-specific effects as "fixed" and "random" effects respectively. Often the same structure of model, which is usually a linear regression model, can be treated as any of the three types depending on the analyst's viewpoint, although there may be a natural choice in any given situation.
In panel data analysis, the term fixed effects estimator (also known as the within estimator) is used to refer to an estimator for the coefficients in the regression model. If we assume fixed effects, we impose time independent effects for each entity that are possibly correlated with the regressors.
Read more about Fixed Effects Model: Qualitative Description, Formal Description, Equality of Fixed Effects (FE) and First Differences (FD) Estimators When T=2, Hausman–Taylor Method, Testing FE Vs. RE, Steps in Fixed Effects Model For Sample Data
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—Marguerite Duras (b. 1914)