Gretl Manual: Gnu Regression, Econometrics and Time-series Library
Prev | Chapter 6. Sub-sampling a dataset | Next

# Restricting the sample

By "restricting" the sample we mean selecting observations on the basis of some Boolean (logical) criterion, or by means of a random number generator. This is likely to be most relevant for cross-sectional or panel data.

Suppose we have data on a cross-section of individuals, recording their gender, income and other characteristics. We wish to select for analysis only the women. If we have a `gender` dummy variable with value 1 for men and 0 for women we could do

    	smpl gender=0 --restrict

to this effect. Or suppose we want to restrict the sample to respondents with incomes over $50,000. Then we could use

    	smpl income>50000 --restrict

A question arises here. If we issue the two commands above in sequence, what do we end up with in our sub-sample: all cases with income over 50000, or just women with income over 50000? By default, in a gretl script, the answer is the latter: women with income over 50000. The second restriction augments the first, or in other words the final restriction is the logical product of the new restriction and any restriction that is already in place.

If you want a new restriction to replace any existing restrictions you can first recreate the full dataset using

          smpl full

Alternatively, you can add the `replace` option to the `smpl` command:

          smpl income>50000 --restrict --replace

This option has the effect of automatically re-establishing the full dataset before applying the new restriction.

Unlike a simple "setting" of the sample, "restricting" the sample may result in selection of non-contiguous observations from the full data set. It may also change the structure of the data set.

This can be seen in the case of panel data. Say we have a panel of five firms (indexed by the variable `firm`) observed in each of several years (identified by the variable `year`). Then the restriction

    	smpl year=1995 --restrict

produces a dataset that is not a panel, but a cross-section for the year 1995. Similarly

    	smpl firm=3 --restrict

produces a time-series dataset for firm number 3.

For these reasons (possible non-contiguity in the observations, possible change in the structure of the data), gretl acts differently when you "restrict" the sample as opposed to simply "setting" it. In the case of setting, the program merely records the starting and ending observations and uses these as parameters to the various commands calling for the estimation of models, the computation of statistics, and so on. In the case of restriction, the program makes a reduced copy of the dataset and by default treats this reduced copy as a simple, undated cross-section.[1] If you wish to re-impose a time-series or panel interpretation of the reduced dataset you can do so using the setobs command, or the GUI menu item "Sample, Dataset structure".

The fact that "restricting" the sample results in the creation of a reduced copy of the original dataset may raise an issue when the dataset is very large (say, several thousands of observations). With such a dataset in memory, the creation of a copy may lead to a situation where the computer runs low on memory for calculating regression results. You can work around this as follows:

1. Open the full data set, and impose the sample restriction.

2. Save a copy of the reduced data set to disk.

3. Close the full dataset and open the reduced one.

4. Proceed with your analysis.

### Notes

[1] | With one exception: if you start with a balanced panel dataset and the restriction is such that it preserves a balanced panel — for example, it results in the deletion of all the observations for one cross-sectional unit — then the reduced dataset is still, by default, treated as a panel.

Prev | Home | Next
Setting the sample | Up | Random sampling