Panels and Time Series Analysis: Markov Chains and Autoregressive Processes

Abstract

Statistical inference in two time series models is developed for cases where there are several observations on the entire time series. Emphasis is on first-order processes: the Markov chain for discrete data and the first- order autoregressive process for vectors of continuous variables. The models are not necessarily homogeneous (or stationary) in time. Sufficient statistics and maximum likelihood estimates are presented. (Continuous variables are assumed normally distributed.) Test criteria for various hypotheses are developed; on a large-sample basis these criteria have chi square-distributions. The close correspondence between properties and statistical methods for the two models is pointed out.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1976
Accession Number
ADA030653

Entities

People

  • Theodore W. Anderson

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Classification
  • Data Science
  • Hypotheses
  • Information Science
  • Markov Chains
  • Measurement
  • Military Research
  • Normal Distribution
  • Observation
  • Probability
  • Random Variables
  • Stationary
  • Statistical Inference
  • Statistics
  • Stochastic Processes
  • Surveys
  • Time Series Analysis

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference