let's say we are interested in the effect of mpg on price for domestic and foreign cars separately. We could split the sample:
Code:
sysuse auto, clear reg price mpg if foreign == 0 reg price mpg if foreign == 1
Code:
reg price c.mpg##foreign margins foreign, dydx(mpg)
Code:
. sysuse auto, clear
(1978 Automobile Data)
. reg price mpg if foreign == 0
Source | SS df MS Number of obs = 52
-------------+---------------------------------- F(1, 50) = 17.05
Model | 124392956 1 124392956 Prob > F = 0.0001
Residual | 364801844 50 7296036.89 R-squared = 0.2543
-------------+---------------------------------- Adj R-squared = 0.2394
Total | 489194801 51 9592054.92 Root MSE = 2701.1
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg | -329.2551 79.74034 -4.13 0.000 -489.4183 -169.0919
_cons | 12600.54 1624.773 7.76 0.000 9337.085 15863.99
------------------------------------------------------------------------------
. reg price mpg if foreign == 1
Source | SS df MS Number of obs = 22
-------------+---------------------------------- F(1, 20) = 13.25
Model | 57534941.7 1 57534941.7 Prob > F = 0.0016
Residual | 86828271.1 20 4341413.55 R-squared = 0.3985
-------------+---------------------------------- Adj R-squared = 0.3685
Total | 144363213 21 6874438.7 Root MSE = 2083.6
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg | -250.3668 68.77435 -3.64 0.002 -393.8276 -106.906
_cons | 12586.95 1760.689 7.15 0.000 8914.217 16259.68
------------------------------------------------------------------------------
Code:
. reg price c.mpg##i.foreign
Source | SS df MS Number of obs = 74
-------------+---------------------------------- F(3, 70) = 9.48
Model | 183435281 3 61145093.6 Prob > F = 0.0000
Residual | 451630115 70 6451858.79 R-squared = 0.2888
-------------+---------------------------------- Adj R-squared = 0.2584
Total | 635065396 73 8699525.97 Root MSE = 2540.1
-------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------+----------------------------------------------------------------
mpg | -329.2551 74.98545 -4.39 0.000 -478.8088 -179.7013
|
foreign |
Foreign | -13.58741 2634.664 -0.01 0.996 -5268.258 5241.084
|
foreign#c.mpg |
Foreign | 78.88826 112.4812 0.70 0.485 -145.4485 303.225
|
_cons | 12600.54 1527.888 8.25 0.000 9553.261 15647.81
-------------------------------------------------------------------------------
. margins foreign, dydx(mpg)
Average marginal effects Number of obs = 74
Model VCE : OLS
Expression : Linear prediction, predict()
dy/dx w.r.t. : mpg
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mpg |
foreign |
Domestic | -329.2551 74.98545 -4.39 0.000 -478.8088 -179.7013
Foreign | -250.3668 83.8404 -2.99 0.004 -417.5812 -83.1524
------------------------------------------------------------------------------
0 Response to Sample split vs. interactions, different t-values
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