000			01600nam a2200265Ia 4500
999			_c95 _d95
003			DE-boiza
005			20191029110128.0
008			190909
020			_a0-19-877313-7s
040			_cIZA
100			_aBauwens, Luc _9260
100			_a Lubrano, Michel _9261
100			_a Richard, Jean François _9262
245		0	_aBayesian Inference in Dynamic Econometric Models
260			_c2000 _bOxford University Press, _aNew York, NY [u.a.],
300			_a350 pages
340			_hC1 55
440			_aAdvanced Texts in Econometrics _94744
520			_aThis book contains an up-to-date coverage of the last twenty years of 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 based on 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 conditional heteroskedastic 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.
650			_aBayesian inference _9265
650			_2econometrics
650			_aeconometric model _9266
856			_uhttps://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780198773122.001.0001/acprof-9780198773122 _yPublisher's website
942			_cBO _2ddc