Hi,
I am working with a rather large panel featuring firm level data. I am trying to analyze path dependence innovation investments. My endogenous variable is binary. I am using Stata 13 and fitted a dynamic random effects discrete choice model. I am further using Wooldrige's "Simple Solution to the Initial Condition Problem" (2005). The latter aside, it basically resembles the following:
I now would like to do two things:
(1) I would like to know, how much variance is explained with the random effect?
(2) I am fitting a few models and would like to know, how many right predictions I get. Is that possible as a postestimation?
Any help is greatly appreciated!
Thank you
/R
I am working with a rather large panel featuring firm level data. I am trying to analyze path dependence innovation investments. My endogenous variable is binary. I am using Stata 13 and fitted a dynamic random effects discrete choice model. I am further using Wooldrige's "Simple Solution to the Initial Condition Problem" (2005). The latter aside, it basically resembles the following:
Code:
webuse nlswork xtset idcode year *First model xtprobit msp l.msp race, vce(robust) *Second model xtprobit msp l.msp race grade, vce(robust)
(1) I would like to know, how much variance is explained with the random effect?
(2) I am fitting a few models and would like to know, how many right predictions I get. Is that possible as a postestimation?
Any help is greatly appreciated!
Thank you
/R