9 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…
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…
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…
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…