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Bayesian model uncertainty in smooth transition autoregressions
Hedibert F. Lopes, Esther Salazar
Research output
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Contribution to journal
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Article
›
peer-review
24
Scopus citations
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Dive into the research topics of 'Bayesian model uncertainty in smooth transition autoregressions'. Together they form a unique fingerprint.
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Mathematics
Reversible Jump Markov Chain Monte Carlo
85%
Model Uncertainty
79%
Information Criterion
74%
Autoregression
74%
Markov Chain Monte Carlo Algorithms
72%
Bayesian Model
67%
Logistics
61%
Deviance Information Criterion
44%
Transition Model
41%
Bayesian Information Criterion
39%
Gibbs Sampler
35%
Real-time
34%
Autoregressive Model
32%
Bayesian Approach
29%
Uncertainty
25%
Methodology
22%
Series
18%
Model
9%
Class
8%
Business & Economics
Smooth Transition Autoregression
100%
Bayesian Model
85%
Information Criterion
74%
Model Uncertainty
71%
Smooth Transition
49%
Markov Chain Monte Carlo
48%
Jump
46%
Logistics
30%
Gibbs Sampler
30%
Bayesian Information Criterion
27%
Deviance
23%
Autoregressive Model
22%
Bayesian Approach
22%
Lag
18%
Uncertainty
11%
Methodology
10%
Engineering & Materials Science
Markov chains
58%
Logistics
54%
Uncertainty
37%
Time series
26%