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Lagged Dependent Variables and Autocorrelation

  • Thomas B. Fomby
  • Stanley R. Johnson
  • R. Carter Hill

Abstract

One of the major purposes of the present chapter is to investigate the effects on ordinary least squares of, first, correlation between a linear model’s regressors and previous error terms and, second, the impact of contemporaneous correlation between the regressors and error term. In the instance of contemporaneous correlation, the sweeping results concerning two-step feasible generalized least squares established in Chapter 8 for the non-stochastic X-case are modified substantially. In particular, estimator efficiency is affected by the choice of \( \hat \Omega \) and the usual generalized least squares formula \(\mathop {{\sigma ^2}}\limits^ \approx {(X'{\hat \Omega ^{ - 1}}X)^{ - 1}} \) understates the variance of the two-step feasible generalized least squares estimator \( \mathop \beta \limits^ \approx = \left( {X'\hat \Omega ^{ - 1} X} \right)^{ - 1} X'\hat \Omega ^{ - 1} y \).

Keywords

Maximum Likelihood Estimator Serial Correlation Consistent Estimate Consistent Estimator Asymptotic Covariance Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1984

Authors and Affiliations

  • Thomas B. Fomby
    • 1
  • Stanley R. Johnson
    • 2
  • R. Carter Hill
    • 3
  1. 1.Department of EconomicsSouthern Methodist UniversityDallasUSA
  2. 2.The Center for Agricultural and Rural DevelopmentIowa State UniversityAmesUSA
  3. 3.Department of EconomicsLouisiana State UniversityBaton RougeUSA

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