What does not concave mean in Stata?

If Stata keeps declaring not concave, that means that the maximization algorithm doesn’t know where to advance next.

What is fixed effect logistic regression?

The fixed effects logistic regression is a conditional model also referred to as a subject-specific model as opposed to being a population-averaged model. The fixed effects logistic regression models have the ability to control for all fixed characteristics (time independent) of the individuals.

What is panel regression used for?

Panel regression is a modeling method adapted to panel data, also called longitudinal data or cross-sectional data. It is widely used in econometrics, where the behavior of statistical units (i.e. panel units) is followed across time. Those units can be firms, countries, states, etc.

What is an Endogeneity problem?

The basic problem of endogeneity occurs when the explanans (X) may be influenced by the explanandum (Y) or both may be jointly influenced by an unmeasured third. The endogeneity problem is one aspect of the broader question of selection bias discussed earlier.

Why is panel data better than others?

Panel data contains more information, more variability, and more efficiency than pure time series data or cross-sectional data. Panel data can detect and measure statistical effects that pure time series or cross-sectional data can’t.

What is meant by fixed effects?

Fixed effects are variables that are constant across individuals; these variables, like age, sex, or ethnicity, don’t change or change at a constant rate over time. They have fixed effects; in other words, any change they cause to an individual is the same.

What is a fixed vs random effect?

The fixed effects are the coefficients (intercept, slope) as we usually think about the. The random effects are the variances of the intercepts or slopes across groups.

Should I ignore the-xtlogit-command?

You can ignore it. As for it taking a long time to run, that’s not surprising: -xtlogit- is quite computationally intensive and in a data set of this size, I would expect it to feel slow on a typical desktop configuration.

Should I ignore the concave/concave message in the output?

As for the (not concave) message, as long as that isn’t present at the final iteration, it doesn’t matter. You can ignore it. As for it taking a long time to run, that’s not surprising: -xtlogit- is quite computationally intensive and in a data set of this size, I would expect it to feel slow on a typical desktop configuration.

Is-xtlogit-doing the job?

Again, -xtlogit- is computationally intensive, and if it just spat out results for you quickly yesterday then it wasn’t doing the job. The combination of the strange results for sigma_u, rho, and the fact that you didn’t find it slow yesterday suggest to me that you had dropped some substantial part of your data set when you ran it yesterday.