Chapter 2The Error Component Model

The error component model is relevant when the slopes, i.e., the marginal effects of the covariates on the response, are the same for all the individuals, the intercepts being a priori different. Note that for some authors, the error component model is a byword for the “random‐effects model” as opposed to the “fixed‐effects model.” These two estimators will be analyzed in this chapter as two different ways to consider the individual component of the error terms for the same error component model (assuming no correlation and correlation with the regressors, respectively).

This is the landmark model of panel data econometrics, and this chapter presents the main results about it.

2.1 Notations and Hypotheses

2.1.1 Notations

For the observation of individual images at period images, we can write the model to be estimated, denoting by images the response, images the vector of images covariates, the error, the intercept, and the vector of parameters associated to the covariates:

(2.1) ...

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