Dear Statalists,
I am running a regular OLS regression of "GDP growth rate" on a series of year dummies, but get exactly the same standard error for all the coefficients, which is puzzling. The details are as follows.
I have a panel data of "GDP growth rate" for a list of countries during 1981-2017. I run a simple regression of the "GDP growth rate" on all the year dummies. In the result table, all the coefficients of the year dummies are different, but they have exactly the same standard errors (no matter I include the constant term or not). My code and results are as below:
reg gdpgrowth i.year
When I predict the residual from the above regression, the residuals are pretty big.
When I sum "GDP growth rate" by year as below, means and s.d. of gdpgrowth are both very different across years.
bysort year: sum gdpgrowth
May I know why I am getting the same s.e. for all the coefficients and why the residuals generated from the regression are very big?
Thanks a lot,
Chenli
I am running a regular OLS regression of "GDP growth rate" on a series of year dummies, but get exactly the same standard error for all the coefficients, which is puzzling. The details are as follows.
I have a panel data of "GDP growth rate" for a list of countries during 1981-2017. I run a simple regression of the "GDP growth rate" on all the year dummies. In the result table, all the coefficients of the year dummies are different, but they have exactly the same standard errors (no matter I include the constant term or not). My code and results are as below:
reg gdpgrowth i.year
gdpgrowth | Coef. | Std. Err. |
year | ||
1982 | -1.779273 | 1.853554 |
1983 | -1.418455 | 1.853554 |
1984 | 2.871273 | 1.853554 |
1985 | 1.883364 | 1.853554 |
1986 | 1.190636 | 1.853554 |
1987 | 0.8425455 | 1.853554 |
1988 | 2.321364 | 1.853554 |
1989 | 2.683545 | 1.853554 |
When I sum "GDP growth rate" by year as below, means and s.d. of gdpgrowth are both very different across years.
bysort year: sum gdpgrowth
May I know why I am getting the same s.e. for all the coefficients and why the residuals generated from the regression are very big?
Thanks a lot,
Chenli
Comment