**SPECIFICATION PROBLEMS: Part 1**

**Omission of a Relevant Variable**

Suppose that the true model is

but through some error in judgement we estimate the parameters of the model

That is we incorrectly omit the k^{th} independent variable. What are the
consequences of having omitted the k^{th} variable? Define

The OLS estimate from the incorrectly specified model is

To check for bias we will substitute in for y

Let us take a closer look at P. Begin with the product of the design matrix with the last
column omitted and the correct design matrix.

Substitute back into the estimator

Now consider the second term. Except for b` _{k}`, it looks like an OLS estimator resulting from
regressing x

So the bias of the least squares estimator for the omitted variables model is

The LS estimator is biased except when either

1. b` _{k}` =
0

or

2. for all i. If this is the case then x

or

3. Suppose x

**Inclusion of an Irrelevant Variable**

No problem, Mon. If an irrelevant variable is included, then in expectation it
takes the value of zero and has no impact on the correctly included variables. Nor, in
expectation, does it change the efficiency of least squares. However, we don't live in a
perfect long run world. Consequently, since including more variables necessarily reduces
the residual sum of squares, the estimate of the coefficient variance will be smaller.
In practice we may find ourselves rejecting too many null hypotheses.