Interactive IV Model #
Preparations #
We load the data, define global macros and set the seed.
. use http://fmwww.bc.edu/repec/bocode/j/jtpa.dta,clear
. global Y earnings
. global D training
. global Z assignmt
. global X sex age married black hispanic
. set seed 42
Step 1: Initialization #
We initialize the model.
. ddml init interactiveiv, kfolds(5)
Step 2: Add learners #
We use stacking (implemented in pystacked) with two base learners for each reduced form equation.
. ddml E[Y|X,Z]: pystacked $Y c.($X)# #c($X), type(reg) m(ols lassocv)
Learner Y1_pystacked added successfully.
. ddml E[D|X,Z]: pystacked $D c.($X)# #c($X), type(class) m(logit lassocv)
Learner D1_pystacked added successfully.
. ddml E[Z|X]: pystacked $Z c.($X)# #c($X), type(class) m(logit lassocv)
Learner Z1_pystacked added successfully.
Step 3-4: Cross-fitting and estimation. #
. ddml crossfit
Cross-fitting E[y|X,Z] equation: earnings
Cross-fitting fold 1 2 3 4 5 ...completed cross-fitting
Cross-fitting E[D|X,Z] equation: training
Cross-fitting fold 1 2 3 4 5 ...completed cross-fitting
Cross-fitting E[Z|X]: assignmt
Cross-fitting fold 1 2 3 4 5 ...completed cross-fitting
. ddml estimate
DDML estimation results (LATE):
spec r Y0 learner Y1 learner D0 learner D1 learner b SE Z learner
opt 1 Y1_pystacked Y1_pystacked D1_pystacked D1_pystacked 1817.206 (512.772) Z1_pystacked
opt = minimum MSE specification for that resample.
E[y|X,D=0] = Y1_pystacked0_1 Number of obs = 11204
E[y|X,D=1] = Y1_pystacked1_1
E[D|X,Z=0] = D1_pystacked0_1
E[D|X,Z=1] = D1_pystacked1_1
E[Z|X] = Z1_pystacked_1
------------------------------------------------------------------------------
| Robust
earnings | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
training | 1817.206 512.7723 3.54 0.000 812.1911 2822.221
------------------------------------------------------------------------------
Short-stacking #
If we wanted to short-stack instead of stack, we proceed as follows.
Initialization:
. set seed 42
. ddml init interactiveiv, kfolds(5)
warning - model m0 already exists
all existing model results and variables will
be dropped and model m0 will be re-initialized
Adding base learners:
. ddml E[Y|X,Z]: reg $Y $X
Learner Y1_reg added successfully.
. ddml E[Y|X,Z]: pystacked $Y c.($X)# #c($X), type(reg) m(lassocv)
Learner Y2_pystacked added successfully.
. ddml E[D|X,Z]: logit $D $X
Learner D1_logit added successfully.
. ddml E[D|X,Z]: pystacked $D c.($X)# #c($X), type(class) m(lassocv)
Learner D2_pystacked added successfully.
. ddml E[Z|X]: logit $Z $X
Learner Z1_logit added successfully.
. ddml E[Z|X]: pystacked $Z c.($X)# #c($X), type(class) m(lassocv)
Learner Z2_pystacked added successfully.
Cross-fitting with shortstack option:
. ddml crossfit, shortstack
Cross-fitting E[y|X,Z] equation: earnings
Cross-fitting fold 1 2 3 4 5 ...completed cross-fitting...completed short-stacking
Cross-fitting E[D|X,Z] equation: training
Cross-fitting fold 1 2 3 4 5 ...completed cross-fitting...completed short-stacking
Cross-fitting E[Z|X]: assignmt
Cross-fitting fold 1 2 3 4 5 ...completed cross-fitting...completed short-stacking
Estimation stage:
. ddml estimate, robust
DDML estimation results (LATE):
spec r Y0 learner Y1 learner D0 learner D1 learner b SE Z learner
opt 1 Y2_pystacked Y2_pystacked D1_logit D2_pystacked 1824.422 (514.303) Z2_pystacked
ss 1 [shortstack] [ss] [ss] [ss] 1818.488 (513.133) [ss]
opt = minimum MSE specification for that resample.
Shortstack DDML model (LATE)
E[y|X,D=0] = earnings_ss0_1 Number of obs = 11204
E[y|X,D=1] = earnings_ss1_1
E[D|X,Z=0] = training_ss0_1
E[D|X,Z=1] = training_ss1_1
E[Z|X] = assignmt_ss_1
------------------------------------------------------------------------------
| Robust
earnings | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
training | 1818.488 513.133 3.54 0.000 812.7663 2824.211
------------------------------------------------------------------------------