# line type: dfbeta (you’ll see each DF and the name of each independent variable – type “list” and then the name of the independent variable you are interested in). For other diagnostics run a regression. For standardized residuals type: predict esta if e(sample), rstandard (in the command line). You will see “esta” appear in the variable list. Now type: list esta and you will see the values. For studentized residuals do try the following after a regression: Predict estu if e(sample), rstudent (estu will now appear as a variable). For Cooks distance type: predict cooksd if e(sample), cooksd after running a regression.

Multicollinearity: after running a regression using regress, type: vif (or: estat vif)

in the command line. Subtract the number in the 1/VIF column from 1

to obtain the percentage of variation in that independent variable which is explained by all other independent variables. In the VIF column, numbers above 30 indicate high variance inflation (i.e., high multicollinearity).

Doesn’t work in probit/logit. Since at this point you’re only interested in multicollinearity, re-estimate a probit/logit equation in regression and then follow the procedure above.

Autocorrelation/Correlogram: regdw (replaces regress command and

executes Durbin-Watson test). The data need to be dated – for example, if your data are annual and you have a variable called year, then before you do a regression type: tsset year and press “enter” (or after running regression with “regress” type dwstat). The command corc (replaces regress command and executes Cochrane-Orcutt correction for first-

order autocorrelation – note data must be dated, see regdw discussion above). You can save the first observation by using the Prais-Winsten (replace “regress” with “prais”). You can obtain a correlogram and specify the number of lags. For example, to obtain a correlogram for the variable “top1” with 12 lags type: corrgram top1, lags(12)

# Heteroscedasticity: run regression replacing regress with fit and then, as a next

# command, type: hettest and press enter. If you have significant heteroscedasticity, use the “robust’ estimation option. Thus, for a “robust” regression type: rreg tax cons party stinc

# Lagged Independent Variable: to lag variable ussr by one time period type:

You can lag a variable one time period by typing “l.” in front of the variable. Thus, l.ussr should be a one period lag of ussr while l2.ussr would be for two time periods back. You can also do this by typing: gen xussr = ussr[_n-1] which will create a new lagged variable: xussr. Remember that your data must be dated (see regdw discussion under Autocorrelation above). Lagging will cost one data point, when you run the regression it running it on your sample minus the first observation. There is an “underline” before the “n-1.”