Hi Edoardo,

For me, Tony Lancaster's "An Introduction to Modern Bayesian Econometrics" is a good textbook. It begins with the basic principle of Bayesian approach, that is, calculation of (prior and posterior) likelihood function, and then applies the principle to wide range of estimation techniques which are very familiar to the classical approach: linear regression: non-linear regression models such as binary choice, ordered multinomial choice, tobit, and count/duration data: panel data analysis:Instrumental variables.

http://www.amazon.com/Introduction-Modern-Bayesian-Econometrics-Lancaster/dp/product-description/1405117206

I think the textbook conforms to your interest because it is more about microeconometrics than time-series (it deals with the latter in just 1 chapter). It does not have so many examples, rather concentrates on a typical example like price-elasticity of gasoline demand, estimation of a production function, relationship between minimum wage and employment, relationship between wage and education and so on. They may not have a direct connection to application to transportation, but still the textbook may be useful because all chapters work with sample codes of S language; it will be a great help if you use S or R.

Thanks,

-Haruki

Hi Edoardo,

I agree with Haruki, the book by Lancaster is good. About the software, I prefer flexible software such as R. See the repository of R and you will find several applications about bayesian econometrics. If you have some programming skills and based on those applications you could do your own scripts.

thanks Aurelio