Dear Statalisters,
I have a panel data on firms and my dependent variable is a discrete choice variable with four alternatives. I have used multinominal logit model to analysis factors affecting firms’ choice behaviour. However, I would like to profit more from cross-sectional time-series data and thought that this is possible using a random effects model.
I am just starting to learn Gllamm and would like to ask if anyone knows about estimating mlogit in the framework of Gllamm. I have being reading the Gllamm manual but I am not sure how should I do it. My explanatory variables do not vary across alternatives and the alternative set is the same for each firm. In order to include random effects do I need to have random effects varying between alternatives? If this is the case, could I use the expanded() option although the explanatory variables are not varying across alternatives?
I would also like to ask whether I have understood the meaning of levels right. If the panel includes information on the firm, industry in which the firm is operating and year(s) when the firm exists in the panel, is it possible to say that these are different levels and to have random effects varying between firms, industries and years?
Finally, could anyone recommend a good reference on mlogit within Gllamm?
I would really appreciate any advice.
Thank’s in advance.
Laura
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
