**SPECIFICATION PROBLEMS: Part 3**

The models discussed in this section are variants of the ANOVA models discussed
earlier. You will see that they are very closely related to the earlier work on
restrictions and tests of hypothesis.

`Dummy Variables `Suppose we have earnings data on workers who can be divided as

Case 1

We also have a continuous variable called x, perhaps age, so the full model is

Using OLS to estimate all the parameters we can then discuss the effects of membership
in particular groups. For example, suppose that we want to predict the earnings of a
non-union, uneducated male. The predicted earnings, conditional on x, are

If we have a union member, educated, single female then the predicted earnings are

The idea is that each group has its own independent intercept, but all have the same
slope on x.

**Case 2
**

We could use dummies to model different slopes for different groups. Consider an
example with just two groups.

We have the model with one RHS variable

If the individual is a male then we get

For a female

The female differs from the male in both intercept and slope. In this case, in which
all of the RHS variables receive the dummy variable treatment, we could apply OLS to the
individual subsets of data and get the same results. In an instance where some slope is
common then we want to apply OLS to the pooled data.

**Case 3
**

Suppose we have a quarterly time series. Instead of setting up a dummy variable for
three of the four quarters as in

we get lazy and construct a variable that takes the following form

Then, blundering along, we estimate the three parameters of

The results by quarter are

First Quarter:

Second Quarter:

Third Quarter:

Fourth Quarter:

The effect of creating this funny RHS variable, S, to account for the seasonal
differences is to make the quarter-to-quarter shift the same between any pair of quarters.
Is this plausible? You will also see this kind of careless construction in cross sections
in which firms in industry 1 have a variable with a value of 1, firms in industry 2 have a
variable with a value of 2 and so on.

**2. Splines**

Splines have their origin in architecture and engineering. In those disciplines a fair
curve needs to be fitted to a set of points and a french curve just won't do the trick.
Instead they use a flexible rubber edge which can be bent to any curve. It is held in
place by weights called ducks. A point where the curve changes its shape is called a knot.

Return to the earlier example in which there were two groups of wage earners differing in
slope and intercept, but the slope did not change as the worker aged. The estimated model
would appear

But suppose, in a new study, we think that the rate of change of wages should depend on
age. The model needs to be modified so that the slope coefficient differs over
prespecified intervals. There is also the stipulation that the resulting curve be
continuous at the points where there is a change in slope. The new model should look like

The function parameters to be estimated are

**Wage = a ^{o} + b^{o}
Age** if Age < 18

**Wage = a ^{1} + b^{1}
Age** if 18 £ Age < 22

**Wage = a ^{2} + b^{2}
Age** if 22 £ Age

To implement this in a form that satisfies the continuity constraint and which is
amenable to least squares let us define

The full model will be

The three slopes are

In order to ensure that the segments meet at the join points we must impose the
following restrictions

This linear spline can be generalized in a couple of ways. First, the segments between the knots can be polynomials. Second, there can be more RHS variables with interactions. This has the effect of fitting planes with different gradients over the space spanned by the RHS variables.

**An interactive look
at dummy variables and splines.**

** **

Specification Problems ToC |
Omitted Variables |
Functional Form |
Non-Nested Hypotheses |

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