Bayesian Inference In Dynamic Econometric Models
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Bayesian Inference in Dynamic Econometric Models
This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques basedon simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditionalheteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.
Bayesian Inference in Dynamic Econometric Models
Offering an up-to-date coverage of the basic principles and tools of Bayesian inference in economics, this textbook then shows how to use Bayesian methods in a range of models suited to the analysis of macroeconomic and financial time series
Bayesian Inference in Dynamic Econometric Models
Offering an up-to-date coverage of the basic principles and tools of Bayesian inference in economics, this textbook then shows how to use Bayesian methods in a range of models suited to the analysis of macroeconomic and financial time series