The use of loops becomes essential when needing to perform repetitive calculations. Looping has many advantages, for example, when needing to do corrections in all the calculations specifications. So here are some interesting features that you would like to do when implementing a loop to run many regressions, and export their outputs:

- Choose the appropriate method for the regression according to the type of dependent variable. For instance, you might want to estimate the model using
*OLS*(regress) when the dependent variable is continuous and or a*probit*or a*logit*model when it is discrete (a dummy variable). - Progressively add explanatory variables to the model and export all the output in a single table. This can be done using
*outreg2*‘s*replace*and*append*options, but if you want instead to write a single command line inside a loop you will have to make the appropriate changes.

So assume that you want to estimate a number of econometric models that are quite similar in terms of the explanatory variables that are incorporated, but differ between them in terms of the dependent variables, for example :

Model 1: outcome1=b1*x1+b2*x2+b3*X3+b4*X4+e

Model 2: outcome2=b1*x1+b2*x2+b3*X3+b4*X4+e

In addition you also want to progressively add sets of explanatory variables. So for instance you want to estimate:

Model 1: outcome1=b1*x1+b2*x2+e

Model 2: outcome1=b1*x1+b2*x2+b3*X3+b4*X4+e

Model 3: outcome2=b1*x1+b2*x2+e

Model 4: outcome2=b1*x1+b2*x2+b3*X3+b4*X4+e

Of course the code should work for n number of explanatory variables and k number of explanatory vars. Let’s do it.

What I am going to do is to first define the sets of explanatory variables using separate globals, for instance: global CONTROLGROUP1 "x1 x2" global CONTROLGROUP2 "x3 x4" * And add the outcome variables into a global global OUTCOMES "outcomevar1 outcomevar2 " /*Start by looping on the types of explanatory variables, in this case we have discrete and continuous variables for each discrete variable we are running a probit model, and for each ordinal variable we will run an ordinal probit model */ global TIPO "disc ord" foreach tipo of global TIPO { /*Here is an interesting feature: using vartyp to separate the outcome variables into groups of discrete and ordinal variables. In this case, the first loop will assign all discrete variables to the global OUTCOMEBYTYPE, the second loop will assign the ordinal variables to the global OUTCOMEBYTYPE and so on. */ vartyp $OUTCOMES, list(`tipo') global OUTCOMEBYTYPE "`r(varlist)'" /*Now assign the appropiate regression methodology to each type of explanatory variable*/ if "`tipo'"=="disc" { local method "dprobit" } else { local method "oprobit" } /*Start the loop of outcomes to be regressed*/ foreach outcome of global OUTCOMEBYTYPE { /*We are not using replace and append when exporting output to tables, so we need to erase the output file if already exists*/ capture confirm file reg_`outcome'.txt if !_rc { erase reg_`outcome'.txt } else { *no action when file not found } /*Start the loop that accumulates explanatory variables. Using while instead of foreach will allow adding more sets of explanatory variables later*/ global i = 1 global CONTROLS "$CONTROLGROUP1" while $i < 4 { /*Finally, the lines for running the regression and exporting the output. It is important to use capture when doing this since if any of the regression ends in an error (for example due to no observations), the loop will continue*/ capture noisily xi: `method' `outcome' $CONTROLS if respondiente==1 [fweight=pesosf] capture noisily outreg2 using reg_`outcome'.txt, append bdec(6) e(all) *The following lines do the trick to add the explanatory sets of variables global i = $i + 1 global CONTROLS "$CONTROLS ${CONTROLGROUP$i}" *Close explanatory variables loop } /* Use this space to add other alternative specifications*/ macro drop CONTROLS macro drop i *Close outcomes loop } *Close type loop }