Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Liana Jacobi"'
Autor:
Wilma E. Waterlander, Tony Blakely, Nhung Nghiem, Christine L. Cleghorn, Helen Eyles, Murat Genc, Nick Wilson, Yannan Jiang, Boyd Swinburn, Liana Jacobi, Jo Michie, Cliona Ni Mhurchu
Publikováno v:
BMC Public Health, Vol 16, Iss 1, Pp 1-13 (2016)
Abstract Background There is a need for accurate and precise food price elasticities (PE, change in consumer demand in response to change in price) to better inform policy on health-related food taxes and subsidies. Methods/Design The Price Experimen
Externí odkaz:
https://doaj.org/article/f1cdfc0b557147339b3f0d5dea18befa
Publikováno v:
Journal of Forecasting. 39:934-943
Large Bayesian vector autoregressions with the natural conjugate prior are now routinely used for forecasting and structural analysis. It has been shown that selecting the prior hyperparameters in a data‐driven manner can often substantially improv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2853890bae8dcd6493a1937a5eff4b2
https://doi.org/10.1002/for.2660
https://doi.org/10.1002/for.2660
Publikováno v:
SSRN Electronic Journal.
Automatic differentiation (AD) is a general method of computing exact derivatives in complex sensitivity analyses and optimisation routines in settings that lack closed-form solutions, thus posing challenges for analytical and numerical alternatives.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::014a40c40df5dd9fa7ac5441600507a4
https://doi.org/10.2139/ssrn.4054947
https://doi.org/10.2139/ssrn.4054947
It is well-known that the marginal likelihood, the gold standard for Bayesian model comparison, can be sensitive to prior hyperparameter choices. However, most models require computationally intense simulation-based methods to evaluate the typically
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98a78a81390f03f96ac525a5d8180aa4
https://hdl.handle.net/10453/162993
https://hdl.handle.net/10453/162993
We propose a new, flexible model for inference of the effect of a binary treatment on a continuous outcome observed over subsequent time periods. The model allows to seperate association due to endogeneity of treatment selection from additional longi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86356a4866eaabd0ef186cf9c689418f
Vector autoregressions (VAR) combined with Minnesota-type priors are widely used for macroeconomic forecasting. The fact that strong but sensible priors can substantially improve forecast performance implies VAR forecasts are sensitive to prior hyper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15972720ac56ac4d37ac5faf0a251ed5
https://doi.org/10.1108/s0731-90532019000040a010
https://doi.org/10.1108/s0731-90532019000040a010
Vector autoregressions (VAR) combined with Minnesota-type priors are widely used for macroeconomic forecasting. The fact that strong but sensible priors can substantially improve forecast performance implies VAR forecasts are sensitive to prior hyper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce57333f90507ef18074250711f23093
https://hdl.handle.net/10453/139794
https://hdl.handle.net/10453/139794
Publikováno v:
SSRN Electronic Journal.
[enter Abstract BThe marginal likelihood is the gold standard for Bayesian model comparison although it is well-known that the value of marginal likelihood could be sensitive to the choice of prior hyperparameters. Most models require computationally
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c138a7967c160a1ab6f42f96b37645d2
https://doi.org/10.2139/ssrn.3409549
https://doi.org/10.2139/ssrn.3409549
Publikováno v:
SSRN Electronic Journal.
Large Bayesian VARs with the natural conjugate prior are now routinely used for forecasting and structural analysis. It has been shown that selecting the prior hyperparameters in a data-driven manner can often substantially improve forecast performan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c01955b05a0305dc0d2d98cff26e6de
https://doi.org/10.2139/ssrn.3409550
https://doi.org/10.2139/ssrn.3409550
Publikováno v:
SSRN Electronic Journal.
Infinitesimal perturbation analysis is a widely used approach to assess the input sensitivities of stochas- tic dynamic systems in the classical simulation context. In this paper, we introduce an efficient nu- merical approach to undertake Infinitesi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ab86d2f88f468eff23ee81f26cfef9a
https://doi.org/10.2139/ssrn.3347399
https://doi.org/10.2139/ssrn.3347399