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Finally, as we briefly mentioned earlier, the estimation precision in Bayesian analysis is not limited
by the sample size—Bayesian simulation methods may provide an arbitrary degree of precision.
Despite the conceptual and methodological advantages of the Bayesian approach, its application in
practice is still considered controversial sometimes. There are two main reasons for this—the presumed
subjectivity in specifying prior information and the computational challenges in implementing Bayesian
methods. Along with the objectivity that comes from the data, the Bayesian approach uses potentially
subjective prior distribution. That is, different individuals may specify different prior distributions.
Proponents of frequentist statistics argue that for this reason, Bayesian methods lack objectivity and
should be avoided. Indeed, there are settings such as clinical trial cases when the researchers want to
minimize a potential bias coming from preexisting beliefs and achieve more objective conclusions.
Even in such cases, however, a balanced and reliable Bayesian approach is possible. The trend in
using noninformative priors in Bayesian models is an attempt to address the issue of subjectivity. On
the other hand, some Bayesian proponents argue that the classical methods of statistical inference
have built-in subjectivity such as a choice for a sampling procedure, whereas the subjectivity is made
explicit in Bayesian analysis.

In Stata 16, we introduce a new, unified suite of commands for modeling choice data. We have added new commands for summarizing choice data. We renamed and improved existing commands for fitting choice models. We even added a new command for fitting mixed logit models for panel data. And we document them together in the new Choice Models Reference Manual.
And here’s the best part: margins now works after fitting choice models. This means you can now easily interpret the results of your choice models. While the coefficients estimated in choice models are often almost uninterpretable, margins allows you to ask and answer very specific questions based on your results. Say that you are modeling choice of transportation. You can answer questions such as
• What proportion of travelers are expected to choose air travel?
• How does the probability of traveling by car change for each additional $10,000 in income?
• If wait times at the airport increase by 30 minutes, how does this affect the choice of each mode of transportation?
What else is new? You now cmset your data before fitting a choice model. For instance,
. cmset personid transportmethod
Then, you use cmsummarize, cmchoiceset, cmtab, and cmsample to explore, summarize, and look for potential problems in your data.
And you use cm estimation commands to fit one of the following choice models:
• cmclogit conditional logit (McFadden’s choice) model
• cmmixlogit mixed logit model
• cmxtmixlogit panel-data mixed logit model
• cmmprobit multinomial probit model
• cmroprobit rank-ordered probit model
• cmrologit rank-ordered logit model
Unlike the others, cmxtmixlogit is not renamed and improved. It is completely new in Stata 16, and

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Stata 16 Feature highlights:
1. Lasso
2. Reporting
3. Meta-analysis
4. Choice models
5. Python integration
6. New in Bayesian analysis—Multiple chains, predictions, and more
7. Panel-data ERMs
8. Import data from SAS and SPSS
9. Nonparametric series regression
10. Multiple datasets in memory
11. Sample-size analysis for confidence intervals
12. Nonlinear DSGE models
13. Multiple-group IRT models
14. xtheckman
15. Multiple-dose pharmacokinetic modeling
16. Heteroskedastic ordered probit models
17. Graph sizes in printer points, centimeters, and inches
18. Numerical integration
19. Linear programming
20. Stata in Korean
21. Mac interface now supports Dark Mode and native tabbed windows
22. Do-file Editor—Autocompletion and more syntax highlighting
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