* There is some debate as to if attenuation bias which biases coefficient estimates toward zero as a result of measurement error reduces the likelihood of rejecting the null.
* It seems to me that it must, but I may be wrong.
* Using the code from the previous post on classical measurement error we can easy simulate this.
cap program drop simME4
program define simME4
* First argument is number of observations
* Second argument is measurement error in the dependent variable
clear
set obs `1' // The first argument defines how many draws
gen weight = rnormal()^2*2
gen v = rnormal()*`2'
gen oweight = weight + v
gen u = rnormal()*10
gen price = 3*weight + u
reg price oweight
test oweight=0
end
simulate p=r(p) b=_b[oweight], rep(2000): simME4 100 0
gen rej = 0
replace rej = 1 if p < .1
sum
* Rejection rate is 100%
simulate p=r(p) b=_b[oweight], rep(2000): simME4 100 20
gen rej = 0
replace rej = 1 if p < .1
sum
* Now are rejection rate is only 24%
* Thus measurement error could place a critical role in failing to reject the null.
Formatted By Econometrics by Simulation
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