Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Dale J. Poirier"'
Autor:
Ivan Jeliazkov, Dale J. Poirier
The volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests. Methodological issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison
Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6294a0fa66dae3c52114cdd52855e180
https://doi.org/10.1017/9781108525947
https://doi.org/10.1017/9781108525947
Autor:
Dale J. Poirier
Publikováno v:
Econometric Reviews. 30:457-468
This article provides Bayesian interpretations for White's heteroskedastic consistent (HC) covariance estimator, and various modifications of it, in linear regression models. An informed Bayesian bootstrap provides a useful framework.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a1251e9a0688e51b95613eb86285ed53
https://doi.org/10.1080/07474938.2011.553542
https://doi.org/10.1080/07474938.2011.553542
Autor:
Dale J. Poirier, Fabio Milani
Publikováno v:
Econometric Reviews. 26:201-204
The paper by An and Schorfheide reviews an important body of literature that takes the empirical implications of Dynamic Stochastic General Equilibrium (DSGE) models seriously. Often, in the past, ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bf608e1f1f871492fcd522f867ecfa9e
https://doi.org/10.1080/07474930701220204
https://doi.org/10.1080/07474930701220204
Autor:
Gary Koop, Dale J. Poirier
Publikováno v:
Journal of Econometrics. 123:259-282
This paper develops new Bayesian methods for semiparametric inference in the partial linear Normal regression model: y=zβ+f(x)+e where f(.) is an unknown function. These methods draw solely on the Normal linear regression model with natural conjugat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1c5a0b4b2b4909dfb728bbf67d5d72c
https://doi.org/10.1016/j.jeconom.2003.12.008
https://doi.org/10.1016/j.jeconom.2003.12.008
Publikováno v:
Journal of Applied Econometrics. 19:203-225
In this paper we describe methods for predicting distributions of outcome gains in the framework of a latent variable selection model. We describe such procedures for Student-t selection models and a finite mixture of Gaussian selection models. Impor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::786b78ddfe6fe492224bc5e070a77075
https://doi.org/10.1002/jae.731
https://doi.org/10.1002/jae.731
Autor:
Justin L. Tobias, Dale J. Poirier
Publikováno v:
Journal of Business & Economic Statistics. 21:258-268
In this article we describe methods for obtaining the predictive distributions of outcome gains in the framework of a standard latent variable selection model. Although most previous work has focused on estimation of mean treatment parameters as the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e934428f128f15ccd43cdf0885a51b3f
https://doi.org/10.1198/073500103288618945
https://doi.org/10.1198/073500103288618945
Autor:
Dale J. Poirier, Kai Li
Publikováno v:
Journal of Econometrics. 113:337-361
This paper presents a new model of the birth process of Native Americans with seven endogenous variables: four birthinputs maternal smoking (S), drinking (D), prenatal care (PC), and weight gain (WG), and three birth outputs gestational age (G), birt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::983dda0e8db6e205e926cacccaaebdd5
https://doi.org/10.1016/s0304-4076(02)00206-3
https://doi.org/10.1016/s0304-4076(02)00206-3
Autor:
Dale J. Poirier
Publikováno v:
Bayesian Inference in the Social Sciences
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db2ec1b3c1e5b36d63fa7009a8ff2b0b
https://doi.org/10.1002/9781118771051.ch12
https://doi.org/10.1002/9781118771051.ch12