*! version 1.0 02july2022

help for ivreg2m

Instrumental variable method to identify treatment-effects estimates with potentially misreported and endogenous program participation

Syntax

ivreg2m depvar [varlist] (treatment=varlist_iv) [weight] [if exp] [in range] [, ta(string) tb(string) options]

Required:

Model
treatment specifies the name of the endogenous misclassified treatment variable.

ta(string) specifies the numeric value(s) of the variable treatment defining the treatment group. The default is ta(1).

tb(string) specifies the numeric value(s) of the variable treatment defining the control group. The default is tb(-1).

Description

ivreg2m can be invoked to estimate the average causal effect for compliers (Imbens and Angrist, 1994) in a traditionally identified single equation when both non-compliance and
misreporting of treatment status are present. The approach follows the estimation procedure developed in Calvi, Lewbel and Tommasi (2021) and Tommasi and Zhang (2022).

The implementation has been built using the frameworks of the existing xtivreg2 (Schaffer, 2020) and ivreg2 (Baum, Schaffer, Stillman, 2015) routines. As ivreg2m is a variant of ivreg2,
almost all of the features and options of ivreg2 are available in ivreg2m. For that reason, you should consult help ivreg2 for details of the available options.

There are three major differences between ivreg2m and ivreg2. First, the ivreg2m command gives an error message if the user utilizes a continuous variable in treatment.
Hence, before running the command, the user must generate a discrete treatment variable taking (at least) three values: (at least) one defining the treatment group (specified in ta),
(at least) one defining the control group (specified in tb), and the rest defining the missing, mismeasured, or unknown status.
Second, there can be one or more instrumental variables, but they must take binary or discrete (integer) values.
Third, only one-way clustering of the VCE is supported.

The ivreg2 and ranktest packages must be installed from the SSC Archive. Earlier versions of ivreg2 should not be used.

Examples

Clear memory and load the 401ksubs dataset

. bcuse 401ksubs, clear

Generate a treatment variable taking (at least) three values:

. generate treat = (p401k & pira)

. replace treat = -1 if (p401k==0 & pira==0)

ivreg2m estimation including misclassified treatment taking 3 values and a binary instrument

. ivreg2m nettfa (treat = e401k), ta(1) tb(-1)

ivreg2m estimation including covariates and robust VCE

. ivreg2m nettfa (treat = e401k) inc, robust ta(1) tb(-1)

ivreg2m estimation including multiple instruments and cluster-robust VCE

. ivreg2m nettfa (treat = e401k pira) inc, cluster(age) ta(1) tb(-1)

References

Baum, C., M. Schaffer, and S. Stillman. 2007. ivreg2: Stata module for extended instrumental variables/2SLS and GMM and AC/HAC, LIML and k-class regression. http://ideas.repec.org/c/boc/bocode/s425401.html

Calvi, R., A. Lewbel, and D. Tommasi. 2021. LATE With Missing or Mismeasured Treatment. Journal of Business & Economic Statistics, forthcoming. https://doi.org/10.1080/07350015.2021.1970573

Imbens, G. W., and J. D. Angrist. 1994. Identification and Estimation of Local Average Treatment Effects. Econometrica 62(2): 467-475.

Schaffer, M. 2020. XTIVREG2: Stata module to perform extended IV/2SLS, GMM and AC/HAC, LIML and k-class regression for panel data models. http://ideas.repec.org/c/boc/bocode/s456501.html

Tommasi, D., and L. Zhang. 2022. Identifying Program Benefits When Participation Is Misreported. IZA Discussion Paper 13430. https://docs.iza.org/dp13430.pdf

Remarks

If you use this command in your work, please cite Calvi, Lewbel and Tommasi (2021) and Tommasi and Zhang (2022).

Authors

Christopher F Baum
Boston College, USA
kit.baum@bc.edu

Denni Tommasi
University of Bologna, Italy
denni.tommasi@monash.edu

Lina Zhang
University of Amsterdam, The Netherlands
l.zhang5@uva.nl