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The least squares estimator of the autoregressive parameter, LS((gamma)), in a first-order stochastic difference equation with independent, identically distributed random innovations is known to be asymptotically unbiased, efficient and consistent (as T ( -->) (INFIN) or (sigma) ( -->) 0) under the proper model specification. Further, LS((gamma)) has a limiting normal distribution around the true parameter, (gamma), if the random innovations are drawn from a normal population. These properties are not observed, however, in sample sizes that are typical of economic time series.
A survey of the field; Mathematical foundations of least-squares prediction theory; Wiener-hopf equations and optimum filters; State-space models and recursive filters.