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.
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Fomby, T.B., Johnson, S.R., Hill, R.C. (1984). Autocorrelation. In: Advanced Econometric Methods. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8746-4_10
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DOI: https://doi.org/10.1007/978-1-4419-8746-4_10
Publisher Name: Springer, New York, NY
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