Fixed Effects With and Without the Social Contrivance of Panel Structure—287
Our new results estimate that the Asian effect is negative (although not significantly so) rather than positive. Our speculation that the positive Asian effect was picking up location effects appears to be correct.
Fixed Effects With and Without the Social Contrivance of Panel Structure
EViews provides a large set of features designed for panel data, but fixed effects estimation is the most important. In terms of econometrics, specifying fixed effects in a linear regression is a fancy way of including a dummy variable for each group (country or state in the examples in this chapter). You’re free to include these dummy variables manually, if you wish.
There is a special circumstance under which including dummies manually is required. Once in a while, you may have three (or more) dimensional panel data. Since EViews panels are limited to two dimensions, the only way to handle a third dimension of fixed effects is by adding dummy variables in that third dimension by hand.
Hint: All else equal, choose the dimension with the fewest categories as the one to be handled manually.
There is a fairly common circumstance under which including dummies manually may be preferred. If all you’re after is fixed effects, why bother setting up a panel structure? Adding dummies into the regression is easier than restructuring a workfile.
288—Chapter 11. Panel—What’s My Line?
There is one circumstance under which you should almost certainly use panel features rather than including dummies manually. If you have lots and lots of categories, panel estimation of fixed effects is much faster. Internally, panel estimation uses a technique called “sweeping out the dummies” to factor out the dummies before running the regression, drastically reducing computational issues. (The time required to compute a linear regression is quadratic in the number of variables.) Additionally, when the number of dummy variables reaches into the hundreds, EViews will sometimes produce regression results using panel estimation for equations in cases in which computation using manually entered dummies breaks down.
Manual dummies—How To
The easy way to include a large number of dummies is through use of the @expand function. @expand was discussed in Chapter 4, “Data—The Transformational Experience,” so here’s a quick review. Add to the least squares command:
@expand(cross_section_identifier,@droplast)
where the option @droplast drops one dummy to avoid the dummy variable trap.
Econometric reminder: The dummy variable trap is what catches you if you attempt to have an intercept and a complete set of dummies in a regression.
For example,
ls lnwage c ed age asian @expand(gmstcen, @droplast)
gives the results to the right.
The regression is identical to our earlier fixed effect results. You do have to remember that the constant term has a different interpretation. In the fixed effect panel estimation, the reported constant is the average ai and the reported fixed effects are the deviations from that average for each category. When using @expand,
Quick Review—Panel—289
the reported constant is the intercept for the dropped category and the reported dummy coefficients are the difference between the category intercept and the intercept for the dropped category.
Quick Review—Panel
The panel feature lets you analyze two dimensional data. Convenient features include prettier identification of your data in spreadsheet views and some extra graphic capabilities. The use of fixed effects in regression is straightforward, and often critical to getting meaningful estimates from regression by washing out unobservables. The examples in this chapter used cross-section fixed effects, but you can use period fixed effects—or both cross section and period fixed effects—just as easily. See Chapter 12, “Everyone Into the Pool” for a different approach to two dimensional data. The User’s Guide describes several advanced statistical tools which can be used with panels.
290—Chapter 11. Panel—What’s My Line?