## Wednesday, September 30, 2015

Some suggestions for the coming month:

## Friday, September 25, 2015

### Thanks, Dan!

Quite out of the blue, Dan Getz kindly sent me a nice LaTeX version of my hand-written copy of "The Solution", given in my last post.

Dan used the ShareLaTeX site - https://www.sharelatex.com/ .

So, here's a nice pdf file with The Solution.

Thanks a million, Dan - that was most thoughtful of you!

## Tuesday, September 22, 2015

### The Solution

You can find a solution to the problem posed in yesterday's post here.

I hope you can read my writing!

p.s.: Dan Getz kindly supplied a LaTeX version - here's the pdf file. Thanks, Dan!

## Monday, September 21, 2015

### Try This Problem

Here's a little exercise for you to work on:

We know from the Gauss-Markhov Theorem that within the class of linear and unbiased estimators, the OLS estimator is most efficient. Because it is unbiased, it therefore has the smallest possible Mean Squared Error (MSE) within the linear and unbiased class of estimators. However, there are many linear estimators which, although biased, have a smaller MSE than the OLS estimator. You might then think of asking:
“Why don’t I try and find the linear estimator that has the smallest possible MSE?”
(a) Show that attempting to do this yields an “estimator” that can’t actually be used in practice.

(You can do this using the simple linear regression model without an intercept, although the result generalizes to the usual multiple linear regression model.)

(b) Now, for the simple regression model with no intercept,

yi = β xi + εi       ;     εi ~ i.i.d. [0 , σ2] ,

find the linear estimator, β* , that minimizes the quantity:

h[Var.(β*) / σ2] + (1 - h)[Bias(β*)/ β]2 , for 0 < h < 1.

Is  β* a legitimate estimator, in the sense that it can actually be applied in practice?