I am still learning Stata and am not sure how to get this to work. I need to run a regression over increasing sample sizes. I know how to get it to run for a specific sample size:
reg y x1 x2 in 1/10
but I need it to do it for sample size 10, then 11, then 12, etc. up to 1000. I tried the following:
foreach var in varlist x1 x2 {
reg y x1 x2 in 10/_n+1
}
but that did not work. How do I get it to loop the regression increasing sample size by 1 each time?
The first answer does exactly what you asked, but you then have the output from (in your example) 991 separate regressions to process. The next question is how to select what you want for further analysis. You could check out official command rolling as providing various machinery or rangestat from SSC. Here's a token reproducible example with a listing of results. The dataset in question is panel data: note that options like by(company) insist on separate regressions for each panel. If you wanted more or different results to be kept, there are related commands.
. webuse grunfeld
. rangestat (reg) invest mvalue, int(year . 0)
+------------------------------------------------------------------------------------------+
| year reg_nobs reg_r2 reg_adj~2 b_mvalue b_cons se_mvalue se_cons |
|------------------------------------------------------------------------------------------|
| 1935 10 .86526037 .84841791 .10253446 .20584135 .01430537 16.364981 |
| 1936 20 .75028331 .73641016 .0893724 7.3202446 .01215286 17.885845 |
| 1937 30 .70628424 .69579439 .08388549 11.163111 .01022308 17.600837 |
| 1938 40 .69788155 .68993107 .08333349 10.545486 .00889458 14.396864 |
| 1939 50 .70399484 .69782807 .08056278 9.3450069 .00754013 12.312899 |
|------------------------------------------------------------------------------------------|
| 1940 60 .72557587 .72084442 .0842278 7.6507759 .0068016 11.294786 |
| 1941 70 .73233666 .72840043 .08992373 7.496037 .00659263 11.034693 |
| 1942 80 .72656572 .72306015 .094125 7.7007269 .00653803 10.706012 |
| 1943 90 .73520447 .73219543 .0968378 6.7626664 .00619519 10.093819 |
| 1944 100 .74792336 .74535115 .09900035 6.0220131 .00580579 9.4585067 |
|------------------------------------------------------------------------------------------|
| 1945 110 .76426375 .762081 .1001161 5.3512756 .00535037 8.8046084 |
| 1946 120 .77316485 .77124251 .10424112 3.9977716 .00519777 8.6559782 |
| 1947 130 .76829138 .76648116 .10701191 4.703102 .0051944 8.549975 |
| 1948 140 .75348635 .75170002 .10927737 6.1833536 .00532076 8.6413437 |
| 1949 150 .75420863 .75254788 .11128353 6.3261435 .00522201 8.4080085 |
|------------------------------------------------------------------------------------------|
| 1950 160 .7520656 .75049639 .114046 5.7698694 .00520945 8.3379321 |
| 1951 170 .75387998 .75241498 .11796668 5.0385173 .00520028 8.4011668 |
| 1952 180 .74822014 .74680565 .12304588 3.702257 .00534999 8.728181 |
| 1953 190 .74683845 .74549185 .1322075 -1.296652 .00561387 9.387601 |
| 1954 200 .734334 .73299225 .14138597 -6.9762842 .00604359 10.272724 |
+------------------------------------------------------------------------------------------+