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Testing the correlated random coefficient model

Heckman, James J.; Schmierer, Daniel; Urzua, Sergio - 2010

The recent literature on instrumental variables (IV) features models in which agents sort into treatment status on the basis of gains from treatment as well as on baselinepretreatment levels. Components of the gains known to the agents and acted on by them may not be known by the observing...

Persistent link: https://www.econbiz.de/10003989944

Uniform post selection inference for LAD regression models

Belloni, Alexandre; Chernozhukov, Victor; Kato, Kengo - 2013

-regular estimation of nuisance part of the regression function, in the sense of Neyman. We establish that in a homoscedastic regression …

Persistent link: https://www.econbiz.de/10009747946

Honest confidence regions for a regression parameter in logistic regression with a large number of controls

Belloni, Alexandre; Chernozhukov, Victor; Wei, Ying - 2013

This paper considers inference in logistic regression models with high dimensional data. We propose new methods for estimating and constructing confidence regions for a regression parameter of primary interest α0, a parameter in front of the regressor of interest, such as the treatment variable...

Persistent link: https://www.econbiz.de/10010226493

Uniform post selection inference for LAD regression and other z-estimation problems

Belloni, Alexandre; Chernozhukov, Victor; Kato, Kengo - 2013

is immunized against non-regular estimation of nuisance part of the median regression function, in the sense of Neyman … interest in a non-smooth Z-estimation framework with approximately sparse nuisance functions, containing median regression with …

Persistent link: https://www.econbiz.de/10010227487

Uniform post selection inference for LAD regression and other Z-estimation problems

Belloni, Alexandre; Chernozhukov, Victor; Kato, Kengo - 2014

model with homoscedastic errors. Our methods are based on a moment equation that is immunized against non-regular estimation … general non-smooth Z-estimation framework with the number of target parameters p1 being possibly much larger than the sample …

Persistent link: https://www.econbiz.de/10010462672

Instrumental variables, local instrumental variables and control functions

Florens, Jean-Paul; Heckman, James J.; Meghir, Costas; … - 2002 - [Elektronische Ressource]

Persistent link: https://www.econbiz.de/10001748192

Estimating the technology of cognitive and noncognitive skill formation

Cunha, Flávio; Heckman, James J.; Schennach, Susanne M. - 2010

This paper formulates and estimates multistage production functions for children's cognitive and noncognitive skills. Skills are determined by parental environments and investments at different stages of childhood. We estimate the elasticity of substitution between investments in one period and...

Persistent link: https://www.econbiz.de/10003989930

Estimation of treatment effects with high-dimensional controls

Belloni, Alexandre; Chernozhukov, Victor; Hansen, … - 2011

controls using Lasso and related methods. We develop estimation and inference methods for the average treatment effect in this … setting, proposing a novel "post double selection" method that provides attractive inferential and estimation properties. In … account for the impact of selection errors on estimation and inference. In order to cover typical applications in economics …

Persistent link: https://www.econbiz.de/10009419338

Bayesian exploratory factor analysis

Conti, Gabriella; Frühwirth-Schnatter, Sylvia; … - 2014

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the...

Persistent link: https://www.econbiz.de/10010382148

Quantile graphical models : prediction and conditional independence with applications to systemic risk

Belloni, Alexandre; Chen, Mingli; Chernozhukov, Victor - 2017 - This version December 4, 2017

The understanding of co-movements, dependence, and influence between variables of interest is key in many applications. Broadly speaking such understanding can lead to better predictions and decision making in many settings. We propose Quantile Graphical Models (QGMs) to characterize prediction...

Persistent link: https://www.econbiz.de/10011775380