David,
thanks for your help. However, I tried to replicate your example and I get stuck at
mat dums=bfdum[1,2,3,4,5,6,7,8,9,10]
which returns "bfdum not found r(111);". (Btw: I guess it should probably read bfdum[2,3,...11], right?]). Why can't I replicate your example? Also: What do you do if some country dummies are dropped due to missing observations? It seems that that your method only works if you can be sure that all cdum1-cdum10 are estimated.
Eric
-----Original Message-----
From: David M. Drukker, Stata Corp [mailto:[email protected]]
Sent: 06 September 2002 15:15
To: [email protected]
Subject: RE: st: How to get country-specific effects as difference from
average effect?
"Neumayer,E" <[email protected]> asked
> If I do "reg y x country2-country207" the country-specific dummy
> variables country2 to country207 are relative to the left out
> category country1. If I do "reg y x country1-country207, noconst"
> instead, the constant is dropped and the country-specific dum my
> variables give each country's level. How do I get Stata to compute
> country-specific estimates as a difference from the average country
> effect? I tried "constraint define 1 country1+...+country207=0" and
> cnsreg, but that does not work.
On the assumption that, in problems like this one, code speaks loader than
words, I have appended two briefly documented examples below.
Method 1:
Save off the estimated dummies from the regression and then subtract their
mean.
Here is an example.
. use grunfeld, clear
. tab company, gen(cdum)
company | Freq. Percent Cum.
------------+-----------------------------------
1 | 20 10.00 10.00
2 | 20 10.00 20.00
3 | 20 10.00 30.00
4 | 20 10.00 40.00
5 | 20 10.00 50.00
6 | 20 10.00 60.00
7 | 20 10.00 70.00
8 | 20 10.00 80.00
9 | 20 10.00 90.00
10 | 20 10.00 100.00
------------+-----------------------------------
Total | 200 100.00
. regress invest mvalue cdum1-cdum10, nocons
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 11, 189) = 148.52
Model | 12208389.6 11 1109853.60 Prob > F = 0.0000
Residual | 1412316.51 189 7472.57415 R-squared = 0.8963
-------------+------------------------------ Adj R-squared = 0.8903
Total | 13620706.1 200 68103.5304 Root MSE = 86.444
------------------------------------------------------------------------------
invest | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mvalue | .1898776 .0179944 10.55 0.000 .1543819 .2253733
cdum1 | -214.8799 80.34482 -2.67 0.008 -373.3677 -56.39209
cdum2 | 36.06969 40.40532 0.89 0.373 -43.63364 115.773
cdum3 | -266.324 39.92423 -6.67 0.000 -345.0784 -187.5697
cdum4 | -45.50152 23.00494 -1.98 0.049 -90.88094 -.1220956
cdum5 | 17.85154 19.77315 0.90 0.368 -21.15287 56.85595
cdum6 | -24.31194 20.75356 -1.17 0.243 -65.2503 16.62642
cdum7 | 19.15374 19.5165 0.98 0.328 -19.34441 57.65189
cdum8 | -84.49925 22.78985 -3.71 0.000 -129.4544 -39.54411
cdum9 | -21.46365 20.24042 -1.06 0.290 -61.38981 18.46251
cdum10 | -10.38181 19.37156 -0.54 0.593 -48.59405 27.83044
------------------------------------------------------------------------------
. mat bfdum = e(b)
. mat dums = bfdum[1,2...]
. mat list dums
dums[1,10]
cdum1 cdum2 cdum3 cdum4 cdum5 cdum6
y1 -214.8799 36.069688 -266.32404 -45.50152 17.851543 -24.31194
cdum7 cdum8 cdum9 cdum10
y1 19.15374 -84.499251 -21.463647 -10.381806
. mat dums_t = dums - J(1,10,1)*(dums*J(10,1,1)/10)
. mat list dums_t
dums_t[1,10]
c1 c2 c3 c4 c5 c6
r1 -155.45118 95.498401 -206.89533 13.927193 77.280256 35.116773
c7 c8 c9 c10
r1 78.582454 -25.070538 37.965067 49.046907
. mat nsum = dums_t*J(10,1,1)
. mat list nsum
symmetric nsum[1,1]
c1
r1 9.237e-14
Method 2:
Another method would be to compute the average effect from the -dums-
vector, subtract this quantity from the orginal dependent variable, and then
re-run the regression.
. mat ave_effect = (dums*J(10,1,1)/10)
. mat list ave_effect
symmetric ave_effect[1,1]
c1
y1 -59.428713
. gen double invest2 = invest-ave_effect[1,1]
. regress invest2 mvalue cdum1-cdum10, nocons
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 11, 189) = 199.33
Model | 16384388.4 11 1489489.85 Prob > F = 0.0000
Residual | 1412316.51 189 7472.57415 R-squared = 0.9206
-------------+------------------------------ Adj R-squared = 0.9160
Total | 17796704.9 200 88983.5243 Root MSE = 86.444
------------------------------------------------------------------------------
invest2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mvalue | .1898776 .0179944 10.55 0.000 .1543819 .2253733
cdum1 | -155.4512 80.34482 -1.93 0.055 -313.939 3.036619
cdum2 | 95.4984 40.40532 2.36 0.019 15.79507 175.2017
cdum3 | -206.8953 39.92423 -5.18 0.000 -285.6497 -128.141
cdum4 | 13.92719 23.00494 0.61 0.546 -31.45223 59.30662
cdum5 | 77.28026 19.77315 3.91 0.000 38.27584 116.2847
cdum6 | 35.11677 20.75356 1.69 0.092 -5.821591 76.05514
cdum7 | 78.58245 19.5165 4.03 0.000 40.0843 117.0806
cdum8 | -25.07054 22.78985 -1.10 0.273 -70.02568 19.8846
cdum9 | 37.96507 20.24042 1.88 0.062 -1.961093 77.89123
cdum10 | 49.04691 19.37156 2.53 0.012 10.83466 87.25915
------------------------------------------------------------------------------
I hope that this helps.
--David
[email protected]
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