Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Beyhum, Jad"'
Autor:
Beyhum, Jad, Mugnier, Martin
We consider a linear panel data model with nonseparable two-way unobserved heterogeneity corresponding to a linear version of the model studied in Bonhomme et al. (2022). We show that inference is possible in this setting using a straightforward two-
Externí odkaz:
http://arxiv.org/abs/2412.07352
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 exogenous or not excluded. We show that a variety of models with discrete or c
Externí odkaz:
http://arxiv.org/abs/2401.03990
Autor:
Beyhum, Jad
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 using a panel of regressors. We propose a procedure to estimate time-
Externí odkaz:
http://arxiv.org/abs/2401.03293
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 continuous) exogenous covariables. By making a rank invariance assumption
Externí odkaz:
http://arxiv.org/abs/2309.02183
We extend nonparametric regression smoothing splines to a context where there is endogeneity and instrumental variables are available. Unlike popular existing estimators, the resulting estimator is one-step and relies on a unique regularization param
Externí odkaz:
http://arxiv.org/abs/2307.14867
Autor:
Beyhum, Jad, Striaukas, Jonas
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 asymptotic properties of the test are established under time series depen
Externí odkaz:
http://arxiv.org/abs/2307.13364
Autor:
Beyhum, Jad, Striaukas, Jonas
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 novel approach integrates sparse and dense dimensionality reduction t
Externí odkaz:
http://arxiv.org/abs/2306.13362
This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outco
Externí odkaz:
http://arxiv.org/abs/2209.01429
The hypothesis of homogeneous treatment effects is central to the instrumental variables literature. This assumption signifies that treatment effects are constant across all subjects. It allows to interpret instrumental variable estimates as average
Externí odkaz:
http://arxiv.org/abs/2208.05344
This paper considers the problem of inferring the causal effect of a variable $Z$ on a dependently censored survival time $T$. We allow for unobserved confounding variables, such that the error term of the regression model for $T$ is correlated with
Externí odkaz:
http://arxiv.org/abs/2208.04184