Estimation in Nonlinear Time Series Models I: Stationary Series.

Abstract

A general framework for analyzing estimates in nonlinear time series models is developed. Ergodic strictly stationary series are treated. General conditions for strong consistency and asymptotic normality are derived both for conditional least squares and maximum likelihood type estimates. Examples are taken from exponential autoregressive, random coefficient autoregressive and bilinear time series models. Some nonstationary models and examples are treated in a sequel to this paper. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1980
Accession Number
ADA145614

Entities

People

  • D. Tjoestheim

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Coefficients
  • Data Science
  • Difference Equations
  • Differential Equations
  • Equations
  • Ergodic Processes
  • Gaussian Processes
  • Information Science
  • Normal Distribution
  • North Carolina
  • Random Variables
  • Real Numbers
  • Statistical Algorithms
  • Statistics
  • Stochastic Processes
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.