Correction: Analysis of human capital effects introducing Bayesian quantile regression in the process of industrial structural upgrading. [PDF]
PLoS OneWang X, Shi S.
europepmc +1 more source
INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles [PDF]
, 2018 Thomas Opitz +3 more
openalex +1 more source
The Journal of Finance, EarlyView.ABSTRACT Using information in returns, we identify the stochastic process of consumption. We find that aggregate consumption reacts over multiple quarters to innovations spanned by financial markets. This persistent component accounts for over a quarter of consumption variation. These shocks command a large and significant risk premium, driving a large
SVETLANA BRYZGALOVA +2 more
wiley +1 more source
A Bayesian quantile regression approach for determining risk factors of low birth weight of under five children in Cambodia. [PDF]
Sci RepHossain MM.
europepmc +1 more source
Robust Bayesian small area estimation based on quantile regression
, 2020 Enrico Fabrizi +2 more
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Journal of Regional Science, Volume 65, Issue 2, Page 403-428, March 2025.ABSTRACT The aim of this article is to exploit an innovative spatial econometric approach to map and study the evolving patterns of industrial districts (IDs). The procedure can be classified as a k‐means cluster‐wise regression procedure and is designed to detect homogeneous areas of subcontracting activity.
Jacopo Canello +3 more
wiley +1 more source
Analysis of human capital effects introducing Bayesian quantile regression in the process of industrial structural upgrading. [PDF]
PLoS OneShi S, Wang X.
europepmc +1 more source
A Bayesian Quantile Regression Analysis of Potential Risk Factors for Violent Crimes in USA
, 2012 Ming Wang, Lijun Zhang
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Solving Stochastic Climate‐Economy Models: A Deep Least‐Squares Monte Carlo Approach
Mathematical Finance, EarlyView.ABSTRACT Stochastic versions of recursive integrated climate‐economy assessment models are essential for studying and quantifying policy decisions under uncertainty. However, as the number of state variables and stochastic shocks increases, solving these models via deterministic grid‐based dynamic programming (e.g., value‐function iteration/projection ...
Aleksandar Arandjelović +4 more
wiley +1 more source