*! version 1.1.1

Title

reporterror -- Executes estimation of the probability masses of an unobserved discrete random variable using two measurements with possibly nonclassical and nonseparable measurement errors.

Syntax

reporterror
x
y
[if] [in]
[, minu(real) maxu(real) nounderreport(real) boot(real)]

Description

reporterror executes estimation of the probability masses of an unobserved discrete random variable using two measurements with possibly nonclassical and nonseparable measurement errors based on
Hu and Sasaki (2017).
The command takes two measurements, x (e.g., self-reported years of education) and y (e.g., sibling-reported years of education), of an unobserved discrete latent variable u (e.g., true years of education), and estimates the probability masses f(u) of u. Also reported are the conditional probabilities of correct reports x and y given u.

Options

minu(real) sets the lower bound of the support of u. Not calling this option automatically sets it to the minimum value of the common support of x and y.

maxu(real) sets the upper bound of the support of u. Not calling this option automatically sets it to the maximum value of the common support of x and y.

nounderreport(real) can be used to toggle between the assumption of no under-reporting and no over-reporting. Not calling this option or setting a non-zero argument in this option implies the assumption of no under-reporting. Calling this option with zero as an argument implies the assumption of no over-reporting

boot(real) sets the number of bootstrap iterations. The default value is boot(2500).

Examples

(u true years of education, selfreport first measurement of u, siblingreport second measurement of u)

Estimation focusing on individuals with high-school diploma u=12 or higher degrees:

. use "example_education.dta"

. reporterror selfreport siblingreport, minu(12)

Stored results

reporterror stores the following in r():

Scalars

r(N) observations

Macros

r(cmd) reporterror

Matrices

r(suppU) support of U

r(fU) mass f(U) of U

r(fX) mass f(X) of X

r(fY) mass f(Y) of Y

r(pXU) conditional probability Pr(X=U|U)

r(pYU) conditional probability Pr(Y=U|U)

Reference

Hu, Y. and Y. Sasaki. 2017. Identification of Paired Nonseparable Measurement Error Models.
Econometric Theory, 33 (4), pp. 955-979.
Link to Paper.

Authors

Yingyao Hu, Johns Hopkins University, Baltimore, MD.

Yuya Sasaki, Vanderbilt University, Nashville, TN.