Maximum Likelihood Estimation of Stochastic Linear Difference Equations with Autoregressive Moving Average Errors.

Abstract

A method is proposed for the estimation of a general class of scalar linear time series models. The model takes the form of a stochastic difference equation for the dependent variable with exogenous variable inputs, and the disturbances are autocorrelated through an autoregressive moving average process. In the present paper an asymptotically efficient yet computationally simple estimation procedure (in the time domain) is derived for this model. The resulting estimator is shown to be asymptotically equivalent to the maximum likelihood estimator and to possess a limiting multivariate normal distribution. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1976
Accession Number
ADA031127

Entities

People

  • Greg Reinsel

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Covariance
  • Data Science
  • Difference Equations
  • Equations
  • Estimators
  • Frequency Domain
  • Information Science
  • Least Squares Method
  • Maximum Likelihood Estimation
  • New York
  • Normal Distribution
  • Probability
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Time Domain
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.