Constructing Confidence Regions for Autoregressive-Model Parameters ()
ABSTRACT
We discuss formulas and techniques for finding maximum-likelihood estimators of parameters of autoregressive (with particular emphasis on Markov and Yule) models, computing their asymptotic variance-covariance matrix and displaying the resulting confidence regions; Monte Carlo simulation is then used to establish the accuracy of the corresponding level of confidence. The results indicate that a direct application of the Central Limit Theorem yields errors too large to be acceptable; instead, we recommend using a technique based directly on the natural logarithm of the likelihood function, verifying its substantially higher accuracy. Our study is then extended to the case of estimating only a subset of a model’s parameters, when the remaining ones (called nuisance) are of no interest to us.
Share and Cite:
Vrbik, J. (2023) Constructing Confidence Regions for Autoregressive-Model Parameters.
Applied Mathematics,
14, 704-717. doi:
10.4236/am.2023.1410042.
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