REGcommand provides a simple yet flexible way compute ordinary least squares regression estimates. Options to the
REGcommand permit the computation of regression diagnostics and two-stage least squares (instrumental variables) estimates.
Applications of regression analysis exist in almost every field. In economics, the dependent variable might be a family's consumption expenditure and the independent variables might be the family's income, number of children in the family, and other factors that would affect the family's consumption patterns. In political science, the dependent variable might be a state's level of welfare spending and the independent variables measures of public opinion and institutional variables that would cause the state to have higher or lower levels of welfare spending. In sociology, the dependent variable might be a measure of the social status of various occupations and the independent variables characteristics of the occupations (pay, qualifications, etc.). In psychology, the dependent variable might be individual's racial tolerance as measured on a standard scale and with indicators of social background as independent variables. In education, the dependent variable might be a student's score on an achievment test and the independent variables characteristics of the student's family, teachers, or school.
The common aspect of the applications described above is that the dependent variable is a quantitative measure of some condition or behavior. When the dependent variable is qualitative or categorical, then other methods (such as logit or probit analysis, described in Chapter 7) might be more appropriate.
First, we assume that the errors u_i have an expected value of zero: E(u_i ) = 0 This means that on average the errors balance out.
Second, we assume that the independent variables are non-random. In an experiment, the values of the independent variable would be fixed by the experimenter and repeated samples could be drawn with the independent variables fixed at the same values in each sample. As a consequence of this assumption, the indenpendent variables will in fact be independent of the disturbance. For non-experimental work, this will need to be assumed directly along with the assumption that the independent variables have finite variances.
Third, we assume that the independent variables are linearly independent.
That is, no independent variable can be expressed as a (non-zero) linear
combination of the remaining independent variables. The failure of this
assumption, known as multicollinearity, clearly makes it infeasible
to disentangle the effects of the supposedly independent variables. If the
independent variables are linearly dependent, SST will produce an error
singularity in independent variables) and abort the
Fourth, we assume that the disturbances u_i are homoscedastic:
This means that the variance of the disturbance is the same for each observation.
Fifth, we assume that the disturbances are not autocorrelated:
This means disturbances associated with different observations are uncorrelated.
If all five of the assumptions above hold, then it can be shown that
the variance of the OLS estimator is given by:
If the independent variables are highly intercorrelated, then the matrix X' X will be nearly singular and the element of (X' X)^-1 will be large, indicating that the estimates of beta may be imprecise.
To estimate Var(b) we require an estimator of sigma^2. It can
be shown that:
is an unbiased estimator of sigma^2. The square root of (sigma hat)^2 is called the standard error of the regression. It is just the standard deviation of the residuals e_i.
There are two important theorems about the properties of the OLS estimators. The Gauss-Markov theorem states that under the five assumptions above, the OLS estimator b is best linear unbiased. That is, the OLS estimator has smaller variance than any other linear unbiased estimator. (One covariance matrix is said to be larger than another if their difference is positive semi-definite.) If we add the assumption that the disturbances u_i have a joint normal distribution, then the OLS estimator has minimum variance among all unbiased estimators (not just linear unbiased estimators).
Although the preceding theorems provide strong justification for using the OLS estimator, it should be realized that OLS is rather sensitive to departures from the assumptions. A few outliers (stray observations generated by a different process) can strongly influence the OLS estimates. SST provides useful diagnostic tools for detecting data problems that we discuss below.
DEPsubop) and one or more independent variables (in the
INDsubop). Unlike some other programs, SST does not automatically add a constant to your independent variables. If you want one, you should create a constant and add it to the list of your independent variables. For example, to regress the variable
xwith an intercept:
set one=1 reg dep[y] ind[one x]
SST will produce two coefficients: an intercept and a slope parameter.
The corresponding regression line passes through the point (0,b_0)
and has slope equal to b_1:
where b_0 is the coefficient of
one and b_1 is the
coefficient of the variable
x. If, on the other hand, you had
omitted the variable
one from the
reg dep[y] ind[x]
SST would produce a "regression through the origin". That is, the
regression line would pass through the point (0,0) with slope
equal to the coefficient of x (b):
For most purposes you will want to include a constant, but SST allows you the flexibility to decide otherwise.
OBS subops can be used to restrict the range of
observations used in the regression. Only the subset of observations
activated by the current
RANGE statement that meet the criteria set
OBS subops will be used. If any of the
variables specified in the
DEP subops have missing
data for an observation, the entire observation is deleted from the
estimation range for that regression. For example, to run a regression of
x (and a constant) including only observations one
reg dep[y] ind[one x] obs[1-10]
OBS subop does not affect the observation range for
SST also allows you to specify multiple dependent variables in the
DEP subop. If one variable is specified in the
subop, it will be regressed on the variables specified in the
IND subop. If
more than one variable is specified in the
DEP subop, separate
regressions will be run for each of these variables on the variables
listed in the
IND subop. The same observation range will
be used for all regressions.
REGcommand using an example taken from David A. Belsey, Edwin Kuh, and Roy E. Welsch, Regression Diagnostics (Wiley, 1980). The variables in the data set are macroeconomic and demographic indicators for fifty countries for the decade of the 1960's and are used to test a simply life cycle savings model.
A text file containing the data is supplied with your SST program disk (bkw.dat). The following variables are in the file:
SR Personal savings rate POP15 Percentage of population under age 15 POP75 Percentage of population over age 75 PDI Personal disposable income per capita (constant dollars) DELPDI Percentage growth rate of PDI from 1960 to 1970
According to the life cycle savings model, savings rates will
be highest among middle-aged individuals. Younger individuals,
anticipating higher incomes as they become older, will have
low savings rates. On the other hand, older individuals will
tend to consume whatever savings they accumulated during middle
age. The following regression equation is proposed to test this
To replicate the Belsey, Kuh, and Welsch analysis, first we
READ the data file
bkw.dat as described in Chapter 2.
SET a variable
one equal to
a vector of ones, and
SAVE the entire data set.
The commands are the following:
range obs[1-100] read to[sr pop15 pop75 dpi deldpi] file[bkw.dat] label var[sr] lab[average personal savings rate] label var[pop15] lab[percentage population under 15] label var[pop75] lab[percentage population over 75] label var[dpi] lab[real disposable income per capita] label var[deldpi] lab[real disposable income growth rate] save file[bkw] set one=1
Now we are ready to regress
reg dep[sr] ind[one pop15 pop75 dpi deldpi]
The output produced is:
********** ORDINARY LEAST SQUARES ********** Dependent Variable: sr Independent Estimated Standard t- Variable Coefficient Error Statistic one 28.5662941 7.3544917 3.8841969 pop15 -0.4611972 0.1446415 -3.1885533 pop75 -1.6914257 1.0835935 -1.5609412 dpi -0.0003371 0.0009311 -0.3620211 deldpi 0.4096868 0.1961958 2.0881534 Number of Observations 50 R-squared 0.338458 Corrected R-squared 0.279655 Sum of Squared Residuals 6.51e+002 Standard Error of the Regression 3.8026627 Mean of Dependent Variable 9.6710000
The OLS estimates provide some support for the life cycle model. The
t-statistic for the coefficient of
pop15 is -3.18, which would
enable us to reject the null hypothesis beta_2 = 0 at conventional
significance levels. The coefficient of
pop75, however, does not
achieve significance at 0.05 level. Notice, also, that the income
effect is small (a thousand dollars of income is associated with only a
0.3 percent rise in the savings rate) and statistically insignificant,
while the income change variable has a positive and statistically significant
impact on the savings rate.
REGprocedure also allows you to produce diagnostic statistics to evaluate the regression estimates including:
Most of these will be familiar, but we discuss in some detail some of the less well known diagnostics: studentized residuals and the hat matrix. These two diagnostics are discussed in detail in Regression Diagnostics.
The hat matrix H is given by:
H = X(X' X)^-1 X'
Note that since:
b = (X' X)^-1 X' y
and by definition:
y hat = Xb
it follows that:
y hat = Hy
Since the hat matrix is of dimension n * n, the number of elements
in it can become quite large. Usually it suffices to work with only the
diagonal elements h_1, ..., h_n:
where x_i is the ith row of the matrix X. Note that:
since I-H is idempotent. It follows therefore:
so that the diagonal elements of the hat matrix are closely related to the variances of the residuals. To compute the studentized residuals, we divide e_i by an estimate of its variance. Rather than using
, we recompute the regression deleting the ith observation. Denote the corresponding estimate of sigma^2 with the ith observation deleted by s^2 (i) and the corresponding diagonal element of the hat matrix from the regression with the ith observation deleted by h_i tilde. The formula for the studentized residual for the ith observation is:
reg dep[sr] ind[one pop15 pop75 dpi deldpi] srsd[studrsd]
Next, we might investigate which observations have large studentized residuals:
print var[studrsd] if[abs(studrsd) > 1.96] OBS VARIABLES studrsd 7: -2.3134 46: 2.8536
Next, we could rerun the regression omitting those observations with large studentized residuals:
reg dep[sr] ind[one pop15 pop75 dpi deldpi] if[abs(studrsd) <= 1.96] ********** ORDINARY LEAST SQUARES ********** Dependent Variable: sr Independent Estimated Standard t- Variable Coefficient Error Statistic one 28.8187711 6.5324171 4.4116551 pop15 -0.4683572 0.1280318 -3.6581323 pop75 -1.5778925 0.9686178 -1.6290146 dpi -0.0003989 0.0008229 -0.4846829 deldpi 0.3480148 0.1740605 1.9993897 Number of Observations 48 R-squared 0.410031 Corrected R-squared 0.355150 Sum of Squared Residuals 4.85e+002 Standard Error of the Regression 3.3589505 Mean of Dependent Variable 9.6747917
In this case the coefficient estimates seem relatively stable with the outliers removed.
IVsubop. Variables in the
IVsubop can overlap with those in the
INDsubop if their are included exogenous variables in the equation. The number of instrumental variables (including included exogenous variables) must be at least as large as the number of independent variables (or else the order condition for identification will not be met).
A simple example of simultaneous equations estimation occurs in
estimating a market supply or demand equation. Consider, for example,
the supply function for an agricultural commodity. Let
the market price of the commodity in each period and
quantity supplied of the commodity. In equilibrium, quantity demanded
and quantity supplied are equal. Moreover,
are simultaneously determined in the market, so it does not make
sense to regress
price to obtain an estimate of the
price elasticity of supply. Suppose, we believe that the supply
function also depends on the weather (measured, say, by
while the demand function also depends on population (
and aggregate personal disposable income (
pdi), but that these
variables are exogenous to the market for this particular commodity.
To estimate the supply equation by two stage least squares, give the
reg dep[quantity] ind[one price weather] iv[one weather populat pdi]
Remember to include the constant
one in the
IV subop, since
it is certainly exogenous. For details of simultaneous equations
estimation, consult any econometrics text, e.g. H. Theil, Principles
of Econometrics (Wiley, 1971), chapters 9-10.