Test Latex

[Steve Samuels]

\(\widehat{\theta}\)

[Steve Samuels]

\(\gamma\)

\(\LATEX\)

[Steve Samuels]

Does this mean that by running a mixed effects model I no longer need to choose between a random effects or a fixed effects with a Hausman?

No. You'll still need to choose between the two types of models

Paul Allison's says in his book "Fixed Effects Regression Methods for Longitudinal Data Using SAS":

"The name “fixed effects” is a source of considerable confusion. As we shall see, the basic idea is very simple. Consider the linear model

\[
Y_{ij} =\beta_0+\beta_1 x_{ij}+ \alpha_i+\epsilon_{ij}
\]

where the i subscript refers to different persons and j refers to different measurements within persons―i.e., the same variable measured at different points in time. In conventional linear model terminology, \( \beta_1 x_{ij}\) is described as a fixed effect because the \(x_{ij}\) terms are all measured values and \( \beta_1 \) is a fixed parameter. On the other hand, \(\epsilon_{ij}\) is regarded as a random variable\(\ldots\) So the typical linear model has both fixed components and random components\(\ldots\)

What about the term \(\alpha_i\), which we use to represent all stable characteristics of persons? Here we have an important choice between treating \(\alpha_i\), as either fixed or random\(\ldots\).

In fixed effects models the \(\alpha_i\) term is treated as a set of fixed parameters, which may either be estimated directly or conditioned out of the estimation process. Hence the name, “fixed effects.”

END ALLISON QUOTE

xtreg, fe will do this model after doing the within transformation, i.e.demaning the variables.

mixed can fit the fixed-effect model with the \(\alpha_i\) in two ways:

1) Demean all the covariates by hand.
2) Add dummy (indicator variables) for individuals. That is, add to the list of covariates the id variables

 i.idcode

You have at least 1,000 individuals, judging from your output in https://www.statalist.org/forums/for...bility-weights.
If you choose the second approach, there will be 1,000 extra lines in the output. About this approach Wikipedia (Fixed effect model) says:

This is numerically, but not computationally, equivalent to the fixed effect model and only works if the sum of the number of series and the number of global parameters is smaller than the number of observations.[9] The dummy variable approach is particularly demanding with respect to computer memory usage and it is not recommended for problems larger than the available RAM, and the applied program compilation, can accommodate.

[Steve Samuels]

test horizontal line

[LINE]80[/Line]

Is this Blue?