Least Squares Estimation of Conditionally Heteroscedastic Autoregressions.

Abstract

The least squares estimate of the autoregressive parameter of a conditionally heteroscedastic autoregression is consistent and asymptotically normal. Failure to recognize conditional heteroscedasticity results in the underestimation of the variance of the least squares estimate, and in extreme cases, this effect can be substantial. The least squares estimate is not asymptotically distribution free, rather, the asymptotic distribution depends on the form of the conditional heteroscedasticity. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1984
Accession Number
ADA145994

Entities

People

  • A. F. L. Nemec

Organizations

  • University of Washington

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Covariance
  • Data Science
  • Engineering
  • Information Science
  • Information Theory
  • Military Research
  • Monte Carlo Method
  • Normality
  • Probability
  • Random Variables
  • Standards
  • Stationary
  • Stationary Processes
  • Statistics
  • Stochastic Processes
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Statistical inference.