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DTSTART:20221106T020000
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DTSTART:20230312T020000
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UID:calendar.25775.field_event_date_2.0@math.wustl.edu
CREATED:20230405T182954Z
DESCRIPTION:Abstract: We develop a semiparametric Bayesian analysis of line
ar regression with possibly endogenous regressors. To avoid the risk of di
stributional misspecification, the prior-posterior analysis is based sole
ly on moment restrictions, and the associated exponentially tilted empiri
cal likelihood. We study the consequences of neglected endogeneity and der
ive a Berstenin-von Mises (BvM) theorem for the posterior distribution of
a (default) base model that assumes that the treatment variables are exoge
nous when that assumption, in fact, is false. Due to the negative conseq
uences of neglected endogeneity, we develop a Bayes factor test for endog
eneity that compares the base model with an extended model that is immune
from the problem of neglected endogeneity. We prove that this test is a co
nsistent selection procedure: as the sample becomes large, it almost sure
ly selects the base model if the treatments are exogenous, and the extend
ed model if the treatments are endogenous. The theory is illustrated with
simulated data, and problems concerning the causal effect of education on
wages, financial asset pricing with potentially endogenous risk factors\
, and the causal effect of (potentially endogenous) airplane ticket prices
on passenger volume.\n\n \n\nHost: Debashis Mondal
DTSTART;TZID=America/Chicago:20230419T150000
DTEND;TZID=America/Chicago:20230419T160000
LAST-MODIFIED:20230405T192029Z
SUMMARY:Data Science and Statistics Seminar: 'Regression Under Endogeneity:
Bernstein-von Mises Theory and Bayes Factors Testing'
URL;TYPE=URI:https://math.wustl.edu/events/data-science-and-statistics-semi
nar-regression-under-endogeneity-bernstein-von-mises-theory
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