Central limit theorem and law of iterated logarithm for least squares algorithms in adaptive tracking

In autoregressive adaptive tracking, we prove that the least squares and the weighted least squares algorithms possess the same asymptotic properties, sharing the same central limit theorem and the same law of iterated logarithm. We also obtain the same asymptotic behavior and show the limitations of these results in the autoregressive with moving average framework.