*! version 1.0 17 Nov 2020

Title

testjfe -- Test for instrument validity in the judge fixed effects design

Syntax

testjfe
varlist
(min=3
numeric)
[if]
[in]
[,
options]

Optional
numknots(#) specifies the number of knots in a the quadratic spline specification of the relationship between the outcome and the instrument propensity

covariates(varlist numeric) specifies variables to be added as linear controls to the regressions calculating instrument propensities and the reduced from regression of outcomes on the instruments

crossvalidate specifies that the number of knots should be chosen by cross validation

dispgamma specifies that the reduced form coefficients on the instrument dummies should be displayed

fitweight(#) specifies the relative weight that should be placed on the fit component of the test as opposed to the slope component of the test. For designs with many judges, higher weight on fit will yield a more powerful test.

graph specifies that a graph be produced showing judge-level average outcomes by judge-level propensity, with the spline-based fit superimposed.

generate(namelist min=3 max=3) specifies that judge-level average outcomes, propensities, and the spline-based fit be generated, and gives the names of the variabgles to be generated. Three variable names are required, in the following order: average outcome, propensity, fit. These variables should not already exist in the data in memory.

Description

testjfe jointly tests the exclusion and monotonicity assumptions invoked in instrumental variables estimation of treatment effects when treatment is a binary indicator and the instruments are a set of mutually exclusive dummy variables.
Stata command crossfold must be installed.

Options

Main

numknots(#) specifies the number of knots in a the quadratic spline specification of the relationship between the outcome and the instrument propensity

covariates(varlist numeric) specifies variables to be added as linear controls to the regressions calculating instrument propensities and the reduced from regression of outcomes on the instruments

crossvalidate specifies that the number of knots should be chosen by cross validation

dispgamma specifies that the reduced form coefficients on the instrument dummies should be displayed

fitweight(#) specifies the relative weight that should be placed on the fit component of the test as opposed to the slope component of the test. For designs with many judges, higher weight on fit will yield a more powerful test.

graph specifies that a graph be produced showing judge-level average outcomes by judge-level propensity, with the spline-based fit superimposed.

generate(namelist min=3 max=3) specifies that judge-level average outcomes, propensities, and the spline-based fit be generated, and gives the names of the variabgles to be generated. Three variable names are required, in the following order: average outcome, propensity, fit. These variables should not already exist in the data in memory.

Examples

testjfe outcome treatment judgeid*, covariates(x*) fitweight(1) generate(ybar pbar yfit) graph

Stored results

Scalars

r(pval) p-value

r(chi2) chi-squared test statistic from the fit component of the test

r(df) degrees of freedom from the fit component of the test

Locals

r(numericalissues)

References

Frandsen, Brigham R., Lars Lefgren, Emily Leslie (2020). Judging Judge Fixed Effects. NBER Working Paper No. 25528.

Author

Dr. Brigham Frandsen, Brigham Young University.

Email frandsen@byu.edu

See Also

Related commands:

crossfold (if installed) ssc install crossfold (to install this command)