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SUMMARY:Semiparametric Instrumental Variables
DTSTART;TZID=Europe/London:20260512T130000
DTEND;TZID=Europe/London:20260512T140000
DTSTAMP:20260512T052620Z
UID:6a96d98d-6219-f111-8342-7c1e522d9057
CREATED:20260306T135830Z
DESCRIPTION:This paper proposes to construct instrumental variables by pro
 jection and expansion. As the projection and expansion process is semipara
 metric\, we define this procedure as a semiparametric instrumental--variab
 le (SIV) method to address endogeneity in regression models without relyin
 g on externally supplied instruments.\n\nThe approach constructs instrumen
 ts directly from observed regressors through a projection-based decomposit
 ion of the structural error. The method applies to linear\, nonlinear\, an
 d non-- and semi--parametric models and provides a simple and computable a
 lternative to conventional instrumental variable approaches.\n\nThis paper
  establishes identification conditions and derives the asymptotic properti
 es of the resulting estimators. It then proposes a simple LASSO selection 
 method to examine the finite--sample performance of both the proposed meth
 od and the established theory by simulated and real data examples.
LAST-MODIFIED:20260508T085043Z
LOCATION:Manor Road Building - Seminar Room G\, Seminar Room G Manor Road 
 Building Manor Road Oxford Oxfordshire OX1 3UQ United Kingdom
SPEAKER:Jiti Gao
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