A MONTE CARLO STUDY OF THE REGRESSION MODEL WITH AUTOCORRELATED DISTURBANCES

Abstract

The paper gives a description of the relative performance of estimators based on the results of a Monte Carlo experiment, under the assumption that disturbances are generated by a first-order autoregressive process. To generate artifical data for the experiment, eight structures were specified. For each structure, 300 samples were drawn and estimates of unknown parameters were calculated for each sample by five different methods, namely, maximum likelihood, Theil-Nager, approximate Bayes, Durbin, and least squares estimators. The task was first to examine the performance of the various estimators and second, to check the behavior of several commonly used tests of independence regression analysis. Characteristics of the various structures were chosen to represent a variety of circumstances that might be reasonably encountered in practical work.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1969
Accession Number
AD0686729

Entities

People

  • Clifford G. Hildreth
  • John Y. Lu

Organizations

  • RAND Corporation

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Autocorrelation
  • Coefficients
  • Computations
  • Computer Programs
  • Computer Science
  • Computers
  • Digital Computers
  • Errors
  • Estimators
  • Frequency
  • Intervals
  • Observation
  • Residuals
  • Statistical Algorithms
  • Statistics

Readers

  • Artificial Intelligence
  • Statistical inference.