. clear
. input str10 Country Year GDP
Country Year GDP
1.
. Angola 1990 100
2.
. Angola 1991 111
3.
. Angola 1992 120
4.
. Bangladesh 1990 240
5.
. Bangladesh 1991 260
6.
. Bangladesh 1992 275
7. end
. gen loggdp = log(GDP)
. egen minyearbycountry = min(Year), by(Country)
. gen yearcounter = Year-minyearbycountry
. gen growthrate = .
(6 missing values generated)
. levelsof Country
`"Angola"' `"Bangladesh"'
. foreach var in `r(levels)' {
2. reg loggdp yearcounter if Country =="`var'"
3. replace growthrate = _b[yearcounter] if Country =="`var'"
4. }
Source | SS df MS Number of obs = 3
-------------+------------------------------ F( 1, 1) = 143.10
Model | .016620574 1 .016620574 Prob > F = 0.0531
Residual | .000116148 1 .000116148 R-squared = 0.9931
-------------+------------------------------ Adj R-squared = 0.9861
Total | .016736722 2 .008368361 Root MSE = .01078
------------------------------------------------------------------------------
loggdp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
yearcounter | .0911608 .0076206 11.96 0.053 -.0056686 .1879901
_cons | 4.60957 .0098382 468.54 0.001 4.484564 4.734576
------------------------------------------------------------------------------
(3 real changes made)
Source | SS df MS Number of obs = 3
-------------+------------------------------ F( 1, 1) = 96.90
Model | .009265993 1 .009265993 Prob > F = 0.0645
Residual | .000095628 1 .000095628 R-squared = 0.9898
-------------+------------------------------ Adj R-squared = 0.9796
Total | .009361621 2 .004680811 Root MSE = .00978
------------------------------------------------------------------------------
loggdp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
yearcounter | .0680661 .0069148 9.84 0.064 -.0197943 .1559265
_cons | 5.484631 .0089269 614.39 0.001 5.371204 5.598058
------------------------------------------------------------------------------
(3 real changes made)
. list Country Year GDP growthrate
+------------------------------------+
| Country Year GDP growth~e |
|------------------------------------|
1. | Angola 1990 100 .0911608 |
2. | Angola 1991 111 .0911608 |
3. | Angola 1992 120 .0911608 |
4. | Bangladesh 1990 240 .0680661 |
5. | Bangladesh 1991 260 .0680661 |
|------------------------------------|
6. | Bangladesh 1992 275 .0680661 |
+------------------------------------+
________________________________________
From: [email protected] [mailto:[email protected]] On Behalf Of Broca, Sumiter (ESDG)
Sent: Tuesday, May 16, 2006 1:22 PM
To: [email protected]
Subject: st: computing growth rates by country
I would appreciate some help on the following problem.
I have time-series data arranged like this, sorted by country year (hypothetical data):
Country Year GDP
Angola 1990 100
Angola 1991 111
Angola 1992 120
Bangladesh 1990 240
Bangladesh 1991 260
Bangladesh 1992 275
.
.
.
(57 countries)
I would like to compute the long run GDP growth rate between 1990 and 1992 for each country by regressing log(GDP(t)) on time (from 1990-92) for that country. The growth rate is given by the coefficient estimate (multiplied by 100 if you want it in percentages). I would like to append this growth rate to the above data set. For this example, I would like the new data set to look like this.
Country Year GDP GDP growth rate, 1990-92 (% p.a.)
Angola 1990 100 9.1
Angola 1991 111 9.1
Angola 1992 120 9.1
Bangladesh 1990 240 6.8
Bangladesh 1991 260 6.8
Bangladesh 1992 275 6.8
.
.
.
In other words, I am looking for an egen type command that calculates a semi-log growth rate for each country and then appends it to the data set for that country. Can someone point me to one?
Thanks in advance.
Sumiter.
Sumiter S. Broca
Economist,
Global Perspectives Studies Unit,
Office of the Assistant Director-General,
Economic and Social Department
Food and Agriculture Organization of the United Nations
Viale delle Terme di Caracalla - 00100 Rome
ITALY
Tel: +39 06 5705 3896
Cell: +39 349 2375 960
Fax: +39 06 5705 6172
email: [email protected]
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