Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)

Rating:
Summary: A really good way to master Dinamic linear models
Review: As a reader with an economical background, mathematical texts are usually hard to be followed. Nevertheless, dinamic models through bayesian forecasting are afordable with this book. Introductory chapters on the bayesian learning algorithm and univariate models rough out the kernel of the issue. Once you dive into the following more complicated chapters you can get lost but the main idea is got. To avoid getting lost, several readings are necessary. Finally, last chapters for non linear models, models with exponential distributions and MCMC methods are really heavy going but a light reading can allow you to get a general overview.

All in all, is a great workbook. The main drawback may be the lack of more practical examples to illustrate the theoretical concepts.