On the Use of Marginal Likelihood in Time Series Model Estimation.

Abstract

This paper is concerned with the estimation of regression models with errors which follow an Autoregressive Integrated Moving Average (ARIMA) process. The effect of the regression upon the ARIMA model parameter estimates is considered and marginal likelihood investigated as a means of overcoming some small sample bias. Examples illustrate the importance of this effect even in samples of moderate size. The consequences regarding inference for the regression coefficients are also discussed.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1983
Accession Number
ADA132828

Entities

People

  • G. Tunnicliffe-wilson

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Coefficients
  • Computational Science
  • Continuous Spectra
  • Contracts
  • Data Science
  • Frequency
  • Frequency Bands
  • Information Science
  • Mathematics
  • Observation
  • Probability
  • Random Walk
  • Spectra
  • Standards
  • Statistical Analysis
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference