Our standard linear model is:

\(y_i=\alpha + X_i\theta +\epsilon_i\)

If we had two sets of data we could view these as:

\(y_{i,0}=\alpha_0 + X_{i,0}\theta_0 +\epsilon_{i,0}\)

\(y_{i,1}=\alpha_1 + X_{i,1}\theta_1 +\epsilon_{i,1}\)

Here, the data data from \(1\) does not affect the parameters in \(2\).

If we think the data generating process is similar between models, then by restricting the freedom of parameters between models we can get more data for each estimate.

For example if we think that all parameters are the same between the models we can estimate:

\(y_{i,0}=\alpha + X_{i,0}\theta +\epsilon_{i,0}\)

\(y_{i,1}=\alpha + X_{i,1}\theta +\epsilon_{i,1}\)

Or:

\(y_{ij}=\alpha + X_{ij}\theta + \epsilon_{ij}\)

Intercepts may be different between the groups. In this case we can instead use the model:

\(y_{ij}=\alpha + X_{ij}\theta + \xi_j + \epsilon_{ij}\)

There are different ways of estimating this model:

Pooled OLS

Fixed effects

Random effects

Our model is:

\(y_{ij}=\alpha + X_{ij}\theta + \xi_j + \epsilon_{ij}\)

We can group the data in two ways, one gets between differences and the other within differences.

In the above example, we could find the effects of schools, or of departments.

\(y_{ij}=\alpha + X_{ij}\theta +\epsilon_{ij}\)

\((y_{ij}-\bar y_{j})=(\alpha -\bar \alpha )+(X_{ij}-\bar X_{j})\theta +(\epsilon_{ij}-\bar \epsilon_{j})\)

\((y_{ij}-\bar y_{j})=(X_{ij}-\bar X_{j})\theta +(\epsilon_{ij}-\bar \epsilon_{j})\)

Or alternatively:

\((y_{ij}-\bar y_{i})=(X_{ij}-\bar X_{i})\theta +(\epsilon_{ij}-\bar \epsilon_{i})\)

Regardless of the form we choose, we can write this as:

\(\ddot y_{ij}=\ddot X_{ij}\theta +\ddot \epsilon_{ij}\)

Our model is:

\(y_{ij}=\alpha + X_{ij}\theta + \xi_j + \epsilon_{ij}\)

With fixed effects we assume that \(U_{ij}\) is a constant for each group. That is:

\(U_{ij}=\delta_{ij}U_j\)

\(y_{ij}=\alpha + X_{ij}\theta +\epsilon_{ij}+\delta_{ij}U_{j}\)

We can use this in a regression if the standard assumptions of OLS are met. In particular, that group membership is uncorrelated with the error term.

We add these dummies to \(X_{ij}\) and regress:

\(y_{ij}=\alpha + X_{ij}\theta +\epsilon_{ij}\)

The parameter for the dummy is the fixed effect of group membership.

As we are including membership in the dependent variables, there is no problem if group membership correlates with other independent variables.

\((y_{ij}-\bar y_{i})=(X_{ij}-\bar X_{i})\theta +(U_{ij} -\bar U_{i}) +(\epsilon_{ij}-\bar \epsilon_{i})\)

Or:

\(\ddot y_{ij}=\ddot X_{ij}\theta +\ddot \epsilon_{ij}\)

This this get the same outcome, but is a different computational process.

Our model is:

\(y_{ij}=\alpha + X_{ij}\theta + \xi_j + \epsilon_{ij}\)

For fixed effects, we had the requirement that group membership be uncorrelated with the error term, but that it could be correlated with other independent variables.

For random effects models, group membership cannot be correlated with other variables.

We have:

\(y_{ij}=\alpha + X_{ij}\theta +\epsilon_{ij}+U_{ij}\)

We now model \(U_{ij}=\bar U_{j}+\rho_j\).

\(y_{ij}=\alpha + X_{ij}\theta +\epsilon_{ij}+\bar U_{j}+\rho_j\)

This randomness of the effect implies, for example, that if we ran the survey again we would expect a different effect

We use GLS.

The Hausman specification test allows you to choose between a fixed effects model and a random effects model.

Random effects models are more efficient.

Used in polls