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pro vyhledávání: '"Kankanala, Sid"'
Autor:
Kankanala, Sid
Latent variable models are widely used to account for unobserved determinants of economic behavior. Traditional nonparametric methods to estimate latent heterogeneity do not scale well into multidimensional settings. Distributional restrictions allev
Externí odkaz:
http://arxiv.org/abs/2311.06831
Autor:
Kankanala, Sid
This paper studies quasi Bayesian estimation and uncertainty quantification for an unknown function that is identified by a nonparametric conditional moment restriction. We derive contraction rates for a class of Gaussian process priors. Furthermore,
Externí odkaz:
http://arxiv.org/abs/2311.00662
Autor:
Kankanala, Sid, Zinde-Walsh, Victoria
Kernel-weighted test statistics have been widely used in a variety of settings including non-stationary regression, inference on propensity score and panel data models. We develop the limit theory for a kernel-based specification test of a parametric
Externí odkaz:
http://arxiv.org/abs/2204.01683
We introduce two data-driven procedures for optimal estimation and inference in nonparametric models using instrumental variables. The first is a data-driven choice of sieve dimension for a popular class of sieve two-stage least squares estimators. W
Externí odkaz:
http://arxiv.org/abs/2107.11869
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