help for kappalate

Title

kappalate -- Estimating the local average treatment effect (LATE) using Abadie's kappa approach and other weighting estimators

Syntax

kappalate
depvar [indepvars] (treatment = instrument) [if] [in] [, options]

zmodel(string) select the approach to estimating the instrument propensity score;
options include logit to fit a logit model by maximum likelihood (ML),
probit to fit a probit model by maximum likelihood (ML),
and cbps to fit a logit model by covariate balancing (CB);
default is cbps

vce(vcetype) vcetype is passed on to Stata's gmm command and specifies the type of standard error reported (see gmm for details); default is robust

std(string) string may be on or off, which determines whether nonbinary covariates are standardized prior to estimation; default is on

which(string) string may be all or norm, which determines whether all estimates or only normalized estimates are displayed; default is norm

Description

kappalate estimates the local average treatment effect (LATE) using methods based on inverse probability weighting, each of which can be motivated by Abadie's (2003) kappa theorem.
See Słoczyński, Uysal, and Wooldridge (2023) for a detailed treatment of the underlying theoretical results.

The following variables should be designated.
depvar is the outcome variable.
indepvars is a list of control variables.
treatment and instrument are the treatment and the instrumental variable, respectively, both of which must be binary and take on only the values 0, 1, or missing.
instrument is assumed to be valid conditional on indepvars and needs to satisfy the monotonicity assumption.

kappalate displays up to five estimates of the LATE, dependent on the options chosen.
The naming convention for the alternative estimates follows Słoczyński, Uysal, and Wooldridge (2023), who recommend normalized estimators (option which(norm)),
and especially the estimator in Uysal (2011), referred to as tau_u, with covariate balancing propensity scores (option zmodel(cbps)), as in Heiler (2022).
We also recommend standardizing nonbinary covariates prior to estimation (option std(on)), as this may improve the performance of Stata's optimization process without affecting the estimation results.

If you use this program in your work, please cite Słoczyński, Uysal, and Wooldridge (2023).

References

Abadie, Alberto (2003). "Semiparametric Instrumental Variable Estimation of Treatment Response Models." Journal of Econometrics 113(2), 231–263.

Heiler, Phillip (2022). "Efficient Covariate Balancing for the Local Average Treatment Effect." Journal of Business & Economic Statistics 40(4), 1569–1582.

Słoczyński, Tymon, S. Derya Uysal, and Jeffrey M. Wooldridge (2023). "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect." arXiv:2204.07672. Available at https://arxiv.org/abs/2204.07672.

Uysal, S. Derya (2011). "Three Essays on Doubly Robust Estimation Methods." PhD dissertation, University of Konstanz.

Examples

. use https://people.brandeis.edu/~tslocz/sipp.dta, clear

. drop if kwage==. | educ==. | rsncode==999

. generate double lwage = ln(kwage)

. kappalate lwage (nvstat=rsncode) age_5, zmodel(cbps) which(norm)

. kappalate lwage (nvstat=rsncode) age_5, zmodel(cbps) which(all)

. kappalate lwage (nvstat=rsncode) age_5, zmodel(logit) which(all)

Stored results

Scalars

e(N) number of observations

Macros

e(cmdline) command as typed

e(cmd) kappalate

e(vce) vcetype specified in vce()

e(zmodel) estimation method for the instrument propensity score specified in zmodel()

e(zvar) name of instrumental variable

e(tvar) name of treatment variable

e(depvar) name of dependent (outcome) variable

e(title) title in estimation output

e(properties) b V

Matrices

e(b) point estimate(s)

e(V) variance-covariance matrix

Functions

e(sample) marks estimation sample

Authors

Derya Uysal, LMU Munich

Email: derya.uysal@econ.lmu.de

Tymon Słoczyński, Brandeis University

Email: tslocz@brandeis.edu

Jeffrey M. Wooldridge, Michigan State University

Email: wooldri1@msu.edu