A Unified Approach to ARMA (Autoregressive-Moving Average) Model Identification and Preliminary Estimation.

Abstract

This paper reviews several different methods for identifying the orders of autoregressive-moving average models for time series data. The case is made that these have a common basis, and that a unified approach may be found in the analysis of a matrix G, defined to be the covariance matrix of forecast values. The estimation of this matrix is considered, emphasis being placed on the use of high order autoregression to approximate the predictor coefficients. Statistical procedures are proposed for analyzing G, and identifying the model orders. A simulation example and three sets of real data are used to illustrate the procedure, which appears to be very useful as a tool for order identification and preliminary model estimation. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1983
Accession Number
ADA136426

Entities

People

  • Davide Piccolo
  • G. T. Wilson

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Autocorrelation
  • Computations
  • Correlation Analysis
  • Covariance
  • Data Science
  • Equations
  • Heuristic Methods
  • Identification
  • Information Science
  • Mathematics
  • Maximum Likelihood Estimation
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Time Series Analysis
  • United States

Fields of Study

  • Engineering
  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Statistical inference.