On Forecasting with Univariate Autoregressive Processes: A Bayesian Approach.

Abstract

Using a normal-gamma prior density for the parameters of a p-th order autoregressive process, the Bayesian predictive density of k future observations is derived. It is shown that the joint predictive density of k future observations may be expressed as the product of k univariate t densities. Our results are illustrated with one-step ahead forecasts employing an AR(1) model with a conjugate prior density for the parameters. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 26, 1982
Accession Number
ADA120838

Entities

People

  • Lyle Broemeling
  • Margaret Land

Organizations

  • Oklahoma State University–Stillwater

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Inference
  • Bayesian Networks
  • Delphi Method
  • Equations
  • Intervals
  • Military Research
  • Models
  • Multivariate Analysis
  • New York
  • Observation
  • Oklahoma
  • Precision
  • Probability
  • Probability Distributions
  • Statistics
  • Theorems
  • Time Series Analysis

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference