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Autocorrelation

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

Abstract

In this chapter we deal with statistical inference in the linear model when it is not appropriate to assume that the random disturbances are uncorrelated. The phenomenon of correlated errors in linear regression models involving time series data is called autocorrelation. Results to follow show that there is much to gain and little to lose by considering alternatives to the independent error assumption of the classical linear regression model. These results are discussed in the context of feasible generalized least squares of Chapter 8.

Keywords

American Statistical Association Error Process Maximum Likelihood Procedure ARMA Process Invertibility Condition 
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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