41 citations · 43 across the 10 of their papers we have counts for
8 papers · 1 filter
Orthogonal Moments in Likelihood Models
Stéphane Bonhomme, Koen Jochmans, Martin Weidner
Many models, such as fixed-effect models for panel or network data, are hard to estimate because they feature nuisance parameters that are both numerous and estimated imprecisely.…
Higher-Order Neyman Orthogonality in Moment-Condition Models
Stéphane Bonhomme, Koen Jochmans, Whitney K. Newey +1
We construct moment functions that are Neyman-orthogonal to a chosen order in parametric moment condition models involving nuisance parameters. These moment functions reduce sensit…
Inference in dynamic models for panel data using the moving block bootstrap
Ayden Higgins, Koen Jochmans
Inference in linear panel data models is complicated by the presence of fixed effects when (some of) the regressors are not strictly exogenous. Under asymptotics where the number o…
A Neyman-Orthogonalization Approach to the Incidental Parameter Problem in Likelihood Models
Stéphane Bonhomme, Koen Jochmans, Martin Weidner
A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parame…
Bootstrap inference for fixed-effect models
Ayden Higgins, Koen Jochmans
The maximum-likelihood estimator of nonlinear panel data models with fixed effects is consistent but asymptotically-biased under rectangular-array asymptotics. The literature has t…
Inference on two component mixtures under tail restrictions
Marc Henry, Koen Jochmans, Bernard Salanié
Many econometric models can be analyzed as finite mixtures. We focus on two-component mixtures and we show that they are nonparametrically point identified by a combination of an e…
Peer effects and endogenous social interactions
Koen Jochmans
We introduce an approach to deal with self-selection of peers in the linear-in-means model. Contrary to the existing proposals we do not require to specify a model for how the sele…
Inference on a Distribution from Noisy Draws
Koen Jochmans, Martin Weidner
We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable. This is common practice…