Weining Wang

Biographic Data

ID8920140
NAMEWeining Wang
GIVEN NAMESWeining
FAMILY NAMEWang
SIGNATUREWANG W
AFFILIATIONSUniversity of York
ORCID0000-0001-7299-6431
VERIFIEDYes
TOTAL WORKS7
TOTAL CITATIONS0
WORKS AS AUTHOR7
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2018
LATEST PUBLICATION YEAR2026
H-INDEX0

  • Uniform Inference on High-Dimensional Spatial Panel Networks

    Open AccessVictor Chernozhukov, Chen Huang et al.ARTICLEJournal of Business and Economic…2026

    We propose employing a high-dimensional generalized method of moments (GMM) estimator, regularized for dimension reduction and subsequently debiased to correct for shrinkage bias (referred to as a debiased-regularized estimator), for inference on large-scale spatial panel networks. In particular, the network structure, which incorporates a flexible sparse deviation that can be regarded either as a latent component or as a misspecification of a pr…

  • Nonlinearity in Dynamic Causal Effects

    Toru Kitagawa, Ning Wang et al.ARTICLEJournal of Business and Economic…2025

    This article was prepared as a comment on “Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly” by Michal Kolesár and Mikkel Plagborg-Møller. We make three comments, including a novel contribution to the literature, showing how a reasonable economic interpretation can potentially be restored for average-effect estimators with negative weights

  • Dynamic Network Quantile Regression Model

    Xiu Xu, Ning Wang et al.ARTICLEJournal of Business and Economic…2024

    We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile …

  • The common and specific components of inflation expectations across European countries

    Open AccessShi Chen, Wolfgang Karl Härdle et al.ARTICLEEmpirical Economics2022

  • Modelling systemic risk using neural network quantile regression

    Open AccessGeorg Keilbar, Ning Wang et al.ARTICLEEmpirical Economics2022

    We propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building on the estimation results, we model systemic risk spillover effects in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the impor…

  • Dynamic Semiparametric Factor Model With Structural Breaks

    Likai Chen, Ning Wang et al.ARTICLEJournal of Business and Economic…2021

    For the change-point analysis of a high-dimensional time series, we consider a semiparametric model with dynamic structural break factors. With our model, the observations are described by a few low-dimensional factors with time-invariant loading functions of the covariates. Regarding the structural break, the factors are assumed to be nonstationary and follow a vector autoregression process with a change in the parameter values. In addition, to …

  • Single-Index-Based CoVaR With Very High-Dimensional Covariates

    Yan Fan, Wolfgang Karl Härdle et al.ARTICLEJournal of Business and Economic…2018

    Systemic risk analysis reveals the interdependencies of risk factors especially in tail event situations. In applications the focus of interest is on capturing joint tail behavior rather than a variation around the mean. Quantile and expectile regression are used here as tools of data analysis. When it comes to characterizing tail event curves one faces a dimensionality problem, which is important for CoVaR (Conditional Value at Risk) determinati…