3 research outputs found
IV Regression with Possibly Uncorrelated Instruments
This paper proposes a closed-form linear IV estimator which allows endogenous
covariates to be weakly correlated or un-correlated but mean-dependent on
instruments. Identification rests on (1) a weak uncorrelatedness exclusion
restriction and (2) a weak relevance condition where covariates are
mean-dependent on instruments. The significant weakening of the relevance
condition does not come at the cost of a stronger exclusion restriction. The
estimator is root-n-consistent and asymptotically normal. Monte Carlo
simulations show the estimator exploits unknown forms of both monotone and
non-monotone identifying variation equally well, and it incurs less bias and
size distortion relative to conventional IV methods when instruments are weak.
An empirical example illustrates the practical usefulness of the estimator
Clustered Covariate Regression
High covariate dimensionality is increasingly occurrent in model estimation,
and existing techniques to address this issue typically require sparsity or
discrete heterogeneity of the unobservable parameter vector. However, neither
restriction may be supported by economic theory in some empirical contexts,
leading to severe bias and misleading inference. The clustering-based grouped
parameter estimator (GPE) introduced in this paper drops both restrictions in
favour of the natural one that the parameter support be compact. GPE exhibits
robust large sample properties under standard conditions and accommodates both
sparse and non-sparse parameters whose support can be bounded away from zero.
Extensive Monte Carlo simulations demonstrate the excellent performance of GPE
in terms of bias reduction and size control compared to competing estimators.
An empirical application of GPE to estimating price and income elasticities of
demand for gasoline highlights its practical utility.Comment: Third draft. Second draft: February 21, 2023. First draft: June 2019.
More simulation results adde