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Default Bayesian Priors for Regression Models with First‐Order Autoregressive Residuals
Author(s) -
Ghosh Malay,
Heo Jungeun
Publication year - 2003
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/1467-9892.00307
Subject(s) - prior probability , autoregressive model , mathematics , frequentist inference , bayesian probability , statistics , econometrics , bayesian linear regression , bayesian inference
. The objective of this paper is to develop default priors when the parameter of interest is the autocorrelation coefficient in normal regression models with first‐order autoregressive residuals. Jeffreys’ prior as well as reference priors are found. These priors are compared in the light of how accurately the coverage probabilities of Bayesian credible intervals match the corresponding frequentist coverage probabilities. It is found that the reference priors have a definite edge over Jeffreys’ prior in this respect. Also, the credible intervals based on these reference priors seem superior to similar intervals based on certain divergence measures.