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Numerical Methods f...
Numerical Methods for Estimation and Inference in Bayesian VAR-models
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- Kadiyala, K Rao (författare)
- Krannert Graduate School of Management, Purdue University, W. Lafayette IN, USA
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- Karlsson, Sune, 1960- (författare)
- Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden
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Krannert Graduate School of Management, Purdue University, W Lafayette IN, USA Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden (creator_code:org_t)
- John Wiley & Sons, 1997
- 1997
- Engelska.
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Ingår i: Journal of applied econometrics (Chichester, England). - : John Wiley & Sons. - 0883-7252 .- 1099-1255. ; 12:2, s. 99-132
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In Bayesian analysis of vector autoregressive models, and especially in forecasting applications, the Minnesota prior of Litterman is frequently used. In many cases other prior distributions provide better forecasts and are preferable from a theoretical standpoint. Several of these priors require numerical methods in order to evaluate the posterior distribution. Different ways of implementing Monte Carlo integration are considered. It is found that Gibbs sampling performs as well as, or better, then importance sampling and that the Gibbs sampling algorithms are less adversely affected by model size. We also report on the forecasting performance of the different prior distributions.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Nationalekonomi (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Economics (hsv//eng)
Nyckelord
- Statistik
- Statistics
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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