Zobrazeno 1 - 10
of 479
pro vyhledávání: '"Gao, Jiti"'
In this paper, we propose a robust estimation and inferential method for high-dimensional panel data models. Specifically, (1) we investigate the case where the number of regressors can grow faster than the sample size, (2) we pay particular attentio
Externí odkaz:
http://arxiv.org/abs/2405.07420
Hierarchical panel data models have recently garnered significant attention. This study contributes to the relevant literature by introducing a novel three-dimensional (3D) hierarchical panel data model, which integrates panel regression with three s
Externí odkaz:
http://arxiv.org/abs/2404.08365
In this paper, we consider estimation and inference for the unknown parameters and function involved in a class of generalized hierarchical models. Such models are of great interest in the literature of neural networks (such as Bauer and Kohler, 2019
Externí odkaz:
http://arxiv.org/abs/2311.02789
To tackle difficulties for theoretical studies in situations involving nonsmooth functions, we propose a sequence of infinitely differentiable functions to approximate the nonsmooth function under consideration. A rate of approximation is established
Externí odkaz:
http://arxiv.org/abs/2309.16348
In this paper, we investigate a semiparametric regression model under the context of treatment effects via a localized neural network (LNN) approach. Due to a vast number of parameters involved, we reduce the number of effective parameters by (i) exp
Externí odkaz:
http://arxiv.org/abs/2306.05593
This paper considers a time-varying vector error-correction model that allows for different time series behaviours (e.g., unit-root and locally stationary processes) to interact with each other to co-exist. From practical perspectives, this framework
Externí odkaz:
http://arxiv.org/abs/2305.17829
Robust M-estimation uses loss functions, such as least absolute deviation (LAD), quantile loss and Huber's loss, to construct its objective function, in order to for example eschew the impact of outliers, whereas the difficulty in analysing the resul
Externí odkaz:
http://arxiv.org/abs/2301.06631
We study the estimation of heterogeneous effects of group-level policies, using quantile regression with interactive fixed effects. Our approach can identify distributional policy effects, particularly effects on inequality, under a type of differenc
Externí odkaz:
http://arxiv.org/abs/2208.03632
We propose a semiparametric method to estimate the average treatment effect under the assumption of unconfoundedness given observational data. Our estimation method alleviates misspecification issues of the propensity score function by estimating the
Externí odkaz:
http://arxiv.org/abs/2206.08503