10 citations · 20 across the 22 of their papers we have counts for
24 papers
Approximate Operator Inversion for Average Effects in Nonlinear Panel Models
Jad Beyhum, Geert Dhaene, Cavit Pakel +1
We study the estimation of average effects in nonlinear panel data models with fixed effects when the time dimension is only moderately large. Our approach, called approximate…
Bootstrap Inference in Nonlinear Panel Data Models with Interactive Fixed Effects
Haoyuan Xu, Wei Miao, Geert Dhaene +1
The maximum likelihood estimator in nonlinear panel data models with interactive fixed effects is biased. Several bias correction methods, such as analytical and jackknife approach…
Estimation of the complier causal hazard ratio under dependent censoring
Gilles Crommen, Jad Beyhum, Ingrid Van Keilegom
In this work, we are interested in studying the causal effect of an endogenous binary treatment on a dependently censored duration outcome. By dependent censoring, it is meant that…
Bounds for the regression parameters in dependently censored survival models
Ilias Willems, Jad Beyhum, Ingrid Van Keilegom
We propose a semiparametric model to study the effect of covariates on the distribution of a censored event time while making minimal assumptions about the censoring mechanism. The…
High-dimensional censored MIDAS logistic regression for corporate survival forecasting
Wei Miao, Jad Beyhum, Jonas Striaukas +1
This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and…
Inference after discretizing time-varying unobserved heterogeneity
Jad Beyhum, Martin Mugnier
Approximating time-varying unobserved heterogeneity by discrete types has become increasingly popular in economics. Yet, provably valid post-clustering inference for target paramet…
Counterfactuals in factor models
Jad Beyhum
We study a new model where the potential outcomes, corresponding to the values of a (possibly continuous) treatment, are linked through common factors. The factors can be estimated…
Identification with possibly invalid IVs
Christophe Bruneel-Zupanc, Jad Beyhum
This paper proposes a novel identification strategy relying on quasi-instrumental variables (quasi-IVs). A quasi-IV is a relevant but possibly invalid IV because it is not exogenou…
Instrumental variable estimation of the proportional hazards model by presmoothing
Lorenzo Tedesco, Jad Beyhum, Ingrid Van Keilegom
We consider instrumental variable estimation of the proportional hazards model of Cox (1972). The instrument and the endogenous variable are discrete but there can be (possibly con…
One-step smoothing splines instrumental regression
Jad Beyhum, Elia Lapenta, Pascal Lavergne
We extend nonparametric regression smoothing splines to a context where there is endogeneity and instrumental variables are available. Unlike popular existing estimators, the resul…
Testing for sparse idiosyncratic components in factor-augmented regression models
Jad Beyhum, Jonas Striaukas
We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative augmenting model with sparse idiosyncratic components. The asy…
Factor-augmented sparse MIDAS regressions with an application to nowcasting
Jad Beyhum, Jonas Striaukas
This article investigates factor-augmented sparse MIDAS (Mixed Data Sampling) regressions for high-dimensional time series data, which may be observed at different frequencies. Our…
Instrumental variable quantile regression under random right censoring
Jad Beyhum, Lorenzo Tedesco, Ingrid Van Keilegom
This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It…
Testing for homogeneous treatment effects in linear and nonparametric instrumental variable models
Jad Beyhum, Jean-Pierre Florens, Elia Lapenta +1
The hypothesis of homogeneous treatment effects is central to the instrumental variables literature. This assumption signifies that treatment effects are constant across all subjec…
An instrumental variable approach under dependent censoring
Gilles Crommen, Jad Beyhum, Ingrid Van Keilegom
This paper considers the problem of inferring the causal effect of a variable on a dependently censored survival time . We allow for unobserved confounding variables, such t…
High-dimensional nonconvex lasso-type -estimators
Jad Beyhum, François Portier
This paper proposes a theory for -norm penalized high-dimensional -estimators, with nonconvex risk and unrestricted domain. Under high-level conditions, the estimators a…
Instrumental variable estimation of dynamic treatment effects on a duration outcome
Jad Beyhum, Samuele Centorrino, Jean-Pierre Florens +1
This paper considers identification and estimation of the causal effect of the time Z until a subject is treated on a survival outcome T. The treatment is not randomly assigned, T…
Two-stage least squares with a randomly right censored outcome
Jad Beyhum
This note develops a simple two-stage least squares (2SLS) procedure to estimate the causal effect of some endogenous regressors on a randomly right censored outcome in the linear…
A nonparametric instrumental approach to endogeneity in competing risks models
Jad Beyhum, Jean-Pierre Florens, Ingrid Van Keilegom
This paper discusses endogenous treatment models with duration outcomes, competing risks and random right censoring. The endogeneity issue is solved using a discrete instrumental v…
High-dimensional inference robust to outliers with l1-norm penalization
Jad Beyhum
This paper studies inference in the high-dimensional linear regression model with outliers. Sparsity constraints are imposed on the vector of coefficients of the covariates. The nu…
Factor and factor loading augmented estimators for panel regression
Jad Beyhum, Eric Gautier
This paper considers linear panel data models where the dependence of the regressors and the unobservables is modelled through a factor structure. The asymptotic setting is such th…
Nonparametric instrumental regression with right censored duration outcomes
Jad Beyhum, Jean-Pierre FLorens, Ingrid Van Keilegom
This paper analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confounding issue is treated using a discrete instrumental var…
Inference robust to outliers with l1-norm penalization
Jad Beyhum
This paper considers the problem of inference in a linear regression model with outliers where the number of outliers can grow with sample size but their proportion goes to 0. We a…
Square-root nuclear norm penalized estimator for panel data models with approximately low-rank unobserved heterogeneity
Jad Beyhum, Eric Gautier
This paper considers a nuclear norm penalized estimator for panel data models with interactive effects. The low-rank interactive effects can be an approximate model and the rank of…