# Indicator Variables

Indicator variables allow us to construct models in which some or all of the parameters of a model can change for subsets of the sample. As discussed in chapter 2, an indicator variable basically indicates whether a certain condition is met. If it does the variable is equal to 1 and if not, it is 0. They are often referred to as dummy variables, and gretl uses this term in a utility that is used to create indicator variables.

The example used in this section is again based on the utown. gdt real estate data. First we will open the dataset and examine the data.

1 open "@gretldirdatapoeutown. gdt"

2 smpl 1 8

3 print price sqft age utown pool fplace —byobs

4 smpl full

5 summary

The sample is limited to the first 8 observations in line 2. The two numbers that follow the smpl command indicate where the subsample begins and where it ends. Logical statements can be used as well to restrict the sample. Examples of this will be given later. In the current case, eight observations are enough to see that price and sqft are continuous, that age is discrete, and that utown, pool, and fplace are likely to be indicator variables. The print statement is used with the —byobs option so that the listed variables are printed in columns.

 price sqft age utown pool fplace 1 205.452 23.46 6 0 0 1 2 185.328 20.03 5 0 0 1 3 248.422 27.77 6 0 0 0 4 154.690 20.17 1 0 0 0 5 221.801 26.45 0 0 0 1 6 199.119 21.56 6 0 0 1 7 272.134 29.91 9 0 0 1 8 250.631 27.98 0 0 0 1

The sample is restored to completeness, and the summary statistics are printed. These give an idea of the range and variability of price, sqft and age. The means tell us about the proportions of homes that are near the University and that have pools or fireplaces.

Summary Statistics, using the observations 1-1000

 Variable Mean Median Minimum Maximum price 247.656 245.833 134.316 345.197 sqft 25.2097 25.3600 20.0300 30.0000 age 9.39200 6.00000 0.000000 60.0000 utown 0.519000 1.00000 0.000000 1.00000 pool 0.204000 0.000000 0.000000 1.00000 fplace 0.518000 1.00000 0.000000 1.00000 Variable Std. Dev. C. V. Skewness Ex. kurtosis price 42.1927 0.170368 0.0905617 -0.667432 sqft 2.91848 0.115768 -0.0928347 -1.18500 age 9.42673 1.00370 1.64752 3.01458 utown 0.499889 0.963177 -0.0760549 -1.99422 pool 0.403171 1.97633 1.46910 0.158242 fplace 0.499926 0.965108 -0.0720467 -1.99481

You can see that half of the houses in the sample are near the University (519/1000). It is also pretty clear that prices are measured in units of \$1000 and square feet in units of 100. The oldest house is 60 years old and there are some new ones in the sample (age=0). Minimums and maximums of 0 and 1, respectively usually mean that you have indicator variables. This confirms what we concluded by looking at the first few observations in the sample.