Polynomial least squares multiple-model estimation: simple, optimal, adaptive, practical

Least squares estimation in a simple random coefficient autoregressive model.∗

The question we discuss is whether a simple random coefficient autoregressive model with infinite variance can create the long swings, or persistence, which are observed in many macro economic variables. The model is defined by yt = stρyt−1 + εt, t = 1, . . . , n, where st is an i.i.d. binary variable with p = P (st = 1), independent of εt i.i.d. with mean zero and finite variance. We say that ...

Least-squares polynomial approximation

We construct symmetric polar WAMs (Weakly Admissible Meshes) with low cardinality for least-squares polynomial approximation on the disk. These are then mapped to an arbitrary triangle. Numerical tests show that the growth of the least-squares projection uniform norm is much slower than the theoretical bound, and even slower than that of the Lebesgue constant of the best known interpolation poi...

An Estimation Of Seasonal GDP Gap In Iran: Application Of Adaptive Least Squares Methods

Least Squares Estimation

Recursive Least Squares Estimation

We start with estimation of a constant based on several noisy measurements. Suppose we have a resistor but do not know its resistance. So we measure it several times using a cheap (and noisy) multimeter. How do we come up with a good estimate of the resistance based on these noisy measurements? More formally, suppose x = (x1, x2, . . . , xn) T is a constant but unknown vector, and y = (y1, y2, ...