Estimation in Nonlinear Time Series Model II: Some Nonstationary Series.

Abstract

In an earlier paper a general framework was introduced for analyzing estimates in stationary nonlinear time series models. In this present paper the framework is enlarged to include certain nonstationary and nonlinear series. General conditions for strong consistency and asymptotic normality are derived both for conditional least squares and maximum likelihood type estimates. Examples are taken from threshold autoregressive, random coefficient autoregressive and doubly stochastic (dynamic state space) models. The emphasis in the examples is on conditional least squares estimates. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1984
Accession Number
ADA145709

Entities

People

  • D. Tjostheim

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Data Science
  • Estimators
  • Identification
  • Information Science
  • Mathematics
  • New York
  • Normality
  • North Carolina
  • Numbers
  • Probability
  • Random Variables
  • Sequences
  • Stationary Processes
  • Statistical Analysis
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Regression Analysis.

Technology Areas

  • Space