How to convert a local string to a string variable and vice versa.

[Alexis Rodas]

Dear everyone,

I would like to know if someone knows a STATA code that I can use to convert string local (for instance, 'local 8 = pop weight land' or 'local 7 pop weight land') to string variable.

The code that I have been working on is the following:

local  1 Index_2018m9(`=tm(2017m1)') Index_2018m9(`=tm(2017m2)')  Index_2018m9(`=tm(2017m3)')  Index_2018m9(`=tm(2017m4)')  ///
         Index_2018m9(`=tm(2017m5)') Index_2018m9(`=tm(2017m6)')  Index_2018m9(`=tm(2017m7)')  Index_2018m9(`=tm(2017m8)')  ///
         Index_2018m9(`=tm(2017m9)') Index_2018m9(`=tm(2017m10)') Index_2018m9(`=tm(2017m11)') Index_2018m9(`=tm(2017m12)') ///
         Index_2018m9(`=tm(2018m1)') Index_2018m9(`=tm(2018m2)')  Index_2018m9(`=tm(2018m3)')  Index_2018m9(`=tm(2018m4)')  ///
         Index_2018m9(`=tm(2018m5)') Index_2018m9(`=tm(2018m6)')  Index_2018m9(`=tm(2018m7)')  Index_2018m9(`=tm(2018m8)')  ///
                                     Index_2018m9(`=tm(2018m10)') Index_2018m9(`=tm(2018m11)') Index_2018m9(`=tm(2018m12)')

local  2 Index_2018m9(`=tm(2017m5)') Index_2018m9(`=tm(2017m6)')  Index_2018m9(`=tm(2017m7)')  Index_2018m9(`=tm(2017m8)')  ///
         Index_2018m9(`=tm(2017m9)') Index_2018m9(`=tm(2017m10)') Index_2018m9(`=tm(2017m11)') Index_2018m9(`=tm(2017m12)') ///
         Index_2018m9(`=tm(2018m1)') Index_2018m9(`=tm(2018m2)')  Index_2018m9(`=tm(2018m3)')  Index_2018m9(`=tm(2018m4)')  ///
         Index_2018m9(`=tm(2018m5)') Index_2018m9(`=tm(2018m6)')  Index_2018m9(`=tm(2018m7)')  Index_2018m9(`=tm(2018m8)')  ///
                                     Index_2018m9(`=tm(2018m10)') Index_2018m9(`=tm(2018m11)') Index_2018m9(`=tm(2018m12)')

local  3 Index_2018m9(`=tm(2015m1)') Index_2018m9(`=tm(2015m2)')   ///
         Index_2018m9(`=tm(2015m5)') Index_2018m9(`=tm(2015m6)')   ///
         Index_2018m9(`=tm(2015m9)') Index_2018m9(`=tm(2015m10)')  ///
         Index_2018m9(`=tm(2016m1)') Index_2018m9(`=tm(2016m2)')   ///
         Index_2018m9(`=tm(2016m5)') Index_2018m9(`=tm(2016m6)')   ///
         Index_2018m9(`=tm(2016m9)') Index_2018m9(`=tm(2016m10)')  ///
         Index_2018m9(`=tm(2017m1)') Index_2018m9(`=tm(2017m2)')   ///
         Index_2018m9(`=tm(2017m5)') Index_2018m9(`=tm(2017m6)')   ///
         Index_2018m9(`=tm(2017m9)') Index_2018m9(`=tm(2017m10)')  ///
         Index_2018m9(`=tm(2018m1)') Index_2018m9(`=tm(2018m2)')   ///
         Index_2018m9(`=tm(2018m5)') Index_2018m9(`=tm(2018m6)')   ///
                                     Index_2018m9(`=tm(2018m10)')

local  4 Index_2018m9(`=tm(2015m3)')  Index_2018m9(`=tm(2015m4)')  ///
         Index_2018m9(`=tm(2015m7)')  Index_2018m9(`=tm(2015m8)')  ///
         Index_2018m9(`=tm(2015m11)') Index_2018m9(`=tm(2015m12)') ///
         Index_2018m9(`=tm(2016m3)')  Index_2018m9(`=tm(2016m4)')  ///
         Index_2018m9(`=tm(2016m7)')  Index_2018m9(`=tm(2016m8)')  ///
         Index_2018m9(`=tm(2016m11)') Index_2018m9(`=tm(2016m12)') ///
         Index_2018m9(`=tm(2017m3)')  Index_2018m9(`=tm(2017m4)')  ///
         Index_2018m9(`=tm(2017m7)')  Index_2018m9(`=tm(2017m8)')  ///
         Index_2018m9(`=tm(2017m11)') Index_2018m9(`=tm(2017m12)') ///
         Index_2018m9(`=tm(2018m3)')  Index_2018m9(`=tm(2018m4)')  ///
         Index_2018m9(`=tm(2018m7)')  Index_2018m9(`=tm(2018m8)')  ///
         Index_2018m9(`=tm(2018m11)') Index_2018m9(`=tm(2018m12)')

local  5 Index_2018m9(`=tm(2015m1)') Index_2018m9(`=tm(2015m3)')   ///
         Index_2018m9(`=tm(2015m5)') Index_2018m9(`=tm(2015m7)')   ///
         Index_2018m9(`=tm(2015m9)') Index_2018m9(`=tm(2015m11)')  ///
         Index_2018m9(`=tm(2016m1)') Index_2018m9(`=tm(2016m3)')   ///
         Index_2018m9(`=tm(2016m5)') Index_2018m9(`=tm(2016m7)')   ///
         Index_2018m9(`=tm(2016m9)') Index_2018m9(`=tm(2016m11)')  ///
         Index_2018m9(`=tm(2017m1)') Index_2018m9(`=tm(2017m3)')   ///
         Index_2018m9(`=tm(2017m5)') Index_2018m9(`=tm(2017m7)')   ///
         Index_2018m9(`=tm(2017m9)') Index_2018m9(`=tm(2017m11)')  ///
         Index_2018m9(`=tm(2018m1)') Index_2018m9(`=tm(2018m3)')   ///
         Index_2018m9(`=tm(2018m5)') Index_2018m9(`=tm(2018m7)')   ///
                                     Index_2018m9(`=tm(2018m11)')

local  6 Index_2018m9(`=tm(2016m5)') Index_2018m9(`=tm(2016m7)')   ///
         Index_2018m9(`=tm(2016m9)') Index_2018m9(`=tm(2016m11)')  ///
         Index_2018m9(`=tm(2017m1)') Index_2018m9(`=tm(2017m3)')   ///
         Index_2018m9(`=tm(2017m5)') Index_2018m9(`=tm(2017m7)')   ///
         Index_2018m9(`=tm(2017m9)') Index_2018m9(`=tm(2017m11)')  ///
         Index_2018m9(`=tm(2018m1)') Index_2018m9(`=tm(2018m3)')   ///
         Index_2018m9(`=tm(2018m5)') Index_2018m9(`=tm(2018m7)')   ///
                                     Index_2018m9(`=tm(2018m11)')

local  7 Index_2018m9(`=tm(2015m1)') ///
         Index_2018m9(`=tm(2015m5)') ///
         Index_2018m9(`=tm(2015m9)') ///
         Index_2018m9(`=tm(2016m1)') ///
         Index_2018m9(`=tm(2016m5)') ///
         Index_2018m9(`=tm(2016m9)') ///
         Index_2018m9(`=tm(2017m1)') ///
         Index_2018m9(`=tm(2017m5)') ///
         Index_2018m9(`=tm(2017m9)') ///
         Index_2018m9(`=tm(2018m1)') ///
         Index_2018m9(`=tm(2018m5)')

local  8 Index_2018m9(`=tm(2015m2)')  Index_2018m9(`=tm(2015m4)')  ///
         Index_2018m9(`=tm(2015m6)')  Index_2018m9(`=tm(2015m8)')  ///
         Index_2018m9(`=tm(2015m10)') Index_2018m9(`=tm(2015m12)') ///
         Index_2018m9(`=tm(2016m2)')  Index_2018m9(`=tm(2016m4)')  ///
         Index_2018m9(`=tm(2016m6)')  Index_2018m9(`=tm(2016m8)')  ///
         Index_2018m9(`=tm(2016m10)') Index_2018m9(`=tm(2016m12)') ///
         Index_2018m9(`=tm(2017m2)')  Index_2018m9(`=tm(2017m4)')  ///
         Index_2018m9(`=tm(2017m6)')  Index_2018m9(`=tm(2017m8)')  ///
         Index_2018m9(`=tm(2017m10)') Index_2018m9(`=tm(2017m12)') ///
         Index_2018m9(`=tm(2018m2)')  Index_2018m9(`=tm(2018m4)')  ///
         Index_2018m9(`=tm(2018m6)')  Index_2018m9(`=tm(2018m8)')  ///
         Index_2018m9(`=tm(2018m10)') Index_2018m9(`=tm(2018m12)')

local  9 Index_2018m9(`=tm(2016m2)')  Index_2018m9(`=tm(2016m4)')  ///
         Index_2018m9(`=tm(2016m6)')  Index_2018m9(`=tm(2016m8)')  ///
         Index_2018m9(`=tm(2016m10)') Index_2018m9(`=tm(2016m12)') ///
         Index_2018m9(`=tm(2017m2)')  Index_2018m9(`=tm(2017m4)')  ///
         Index_2018m9(`=tm(2017m6)')  Index_2018m9(`=tm(2017m8)')  ///
         Index_2018m9(`=tm(2017m10)') Index_2018m9(`=tm(2017m12)') ///
         Index_2018m9(`=tm(2018m2)')  Index_2018m9(`=tm(2018m4)')  ///
         Index_2018m9(`=tm(2018m6)')  Index_2018m9(`=tm(2018m8)')  ///
         Index_2018m9(`=tm(2018m10)') Index_2018m9(`=tm(2018m12)')

local 10 Index_2018m9(`=tm(2015m2)')   ///
         Index_2018m9(`=tm(2015m6)')   ///
         Index_2018m9(`=tm(2015m10)')  ///
         Index_2018m9(`=tm(2016m2)')   ///
         Index_2018m9(`=tm(2016m6)')   ///
         Index_2018m9(`=tm(2016m10)')  ///
         Index_2018m9(`=tm(2017m2)')   ///
         Index_2018m9(`=tm(2017m6)')   ///
         Index_2018m9(`=tm(2017m10)')  ///
         Index_2018m9(`=tm(2018m2)')   ///
         Index_2018m9(`=tm(2018m6)')   ///
         Index_2018m9(`=tm(2018m10)') 

local predictores 1 2 3 4 5 6 7 8 9 10

Those "locals", I use them as independent variables in a regression. I made many regressions. From those regressions, I chose the best regression and therefore I know what "local" number (for instance, "local 8") was used for that regression (for example, DID regression).
Now, I want to run another regression (for example, data panel) with the independent variables that were used for the best DID regression.
I do not know how to use the same "local" (for instance, "local 8") I used in a loop, but now for another regression.

It occurs to me to convert the "local" to a variable string and when I want to use the "local 8" (now in a variable string), I will convert the variable string to "local" one. But I do not know how.

I hope I didn't confuse you.

Thanks a lot for your help

Alexis Rodas

[William Lisowski]

I will start by saying that my preference is to use macro names that start with a letter. So in the very simple example code below I have done that, and I hope it points you in a useful direction. My understanding of what you hope to accomplish may not be correct.

. // use my sample data
. sysuse auto, clear
(1978 Automobile Data)

. 
. // define two lists of independent variables as local macros named v1 and v2
. local v1 weight length

. local v2 weight mpg 

. 
. // use a loop to fit regressions trying each set of independent variables
. forvalues i=1/2 {
  2.     display " regression: `i' " _newline " list: v`i'  variables: `v`i'' "
  3.     regress price `v`i''
  4. }
 regression: 1 
 list: v1  variables: weight length 

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(2, 71)        =     18.91
       Model |   220725280         2   110362640   Prob > F        =    0.0000
    Residual |   414340116        71  5835776.28   R-squared       =    0.3476
-------------+----------------------------------   Adj R-squared   =    0.3292
       Total |   635065396        73  8699525.97   Root MSE        =    2415.7

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      weight |   4.699065   1.122339     4.19   0.000     2.461184    6.936946
      length |  -97.96031    39.1746    -2.50   0.015    -176.0722   -19.84838
       _cons |   10386.54   4308.159     2.41   0.019     1796.316    18976.76
------------------------------------------------------------------------------
 regression: 2 
 list: v2  variables: weight mpg 

      Source |       SS           df       MS      Number of obs   =        74
-------------+----------------------------------   F(2, 71)        =     14.74
       Model |   186321280         2  93160639.9   Prob > F        =    0.0000
    Residual |   448744116        71  6320339.67   R-squared       =    0.2934
-------------+----------------------------------   Adj R-squared   =    0.2735
       Total |   635065396        73  8699525.97   Root MSE        =      2514

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      weight |   1.746559   .6413538     2.72   0.008      .467736    3.025382
         mpg |  -49.51222   86.15604    -0.57   0.567    -221.3025     122.278
       _cons |   1946.069    3597.05     0.54   0.590    -5226.245    9118.382
------------------------------------------------------------------------------

. 
. // run the best model again on only foreign cars
. 
. local vars

. display " new regression " _newline " list: v1  variables: `v1' "
 new regression 
 list: v1  variables: weight length 

. regress price `v1' if foreign==1

      Source |       SS           df       MS      Number of obs   =        22
-------------+----------------------------------   F(2, 19)        =     34.72
       Model |   113350528         2    56675264   Prob > F        =    0.0000
    Residual |  31012684.7        19  1632246.56   R-squared       =    0.7852
-------------+----------------------------------   Adj R-squared   =    0.7626
       Total |   144363213        21   6874438.7   Root MSE        =    1277.6

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      weight |   4.937235   1.557596     3.17   0.005     1.677149    8.197321
      length |   14.76399   49.29231     0.30   0.768      -88.406     117.934
       _cons |  -7537.909   5247.076    -1.44   0.167    -18520.17    3444.348
------------------------------------------------------------------------------