![]() ![]() ![]() churdle linear hours age, select(commute) ll(0)Ĭragg hurdle regression Number of obs = 1,983 We consider a simple hurdle model using a subset of the fitness dataset from churdle: This can be achieved by specifying churdle‘s options from() and iterate(0). One can write a program to compute the log likelihood of the double hurdle model and use this program with bayesmh (in the spirit of ), but this may seem like a daunting task if you are not familiar with Stata programming.Īnd then we realized, why not simply call churdle from the evaluator to compute the log likelihood? All we need is for churdle to evaluate the log likelihood at specific values of model parameters without performing iterations. Our initial reaction to this question was ‘No’ or, more precisely, ‘Not easily’-hurdle models are not among the likelihood models supported by bayesmh. This post was written jointly with Nikolay Balov, Senior Statistician and Software Developer, StataCorp.Ī question on Statalist motivated us to write this blog entry.Ī user asked if the churdle command ( ) for fitting hurdle models, new in Stata 14, can be combined with the bayesmh command ( ) for fitting Bayesian models, also new in Stata 14: ![]()
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