This paper presents a novel model comparison to examine the challenges posed by changes in carbon-intensive energy prices for monetary policy. The employed environmental monetary models have a detailed multi-sector structure. The comparison assesses the effects of both a temporary and a...

This work presents a practical approach to improve risk quantification for heavy-tailed insurance claims through model averaging and grid map visualization, addressing the drawbacks of traditional single "best" model selection commonly used in actuarial and model-fitting literature. This is a...

Accurate supplier lead time prediction is critical for maintaining resilience in Engineer-to-Order (EtO) supply chains, characterized by high customization and uncertainty. This study develops a simulation-based predictive framework combining log-normal sensitivity analysis, Internet of Things...

Selecting the relevant covariates in high-dimensional panel data remains a central challenge in applied econometrics. Conventional fixed effects and random effects models are not designed for systematic variable selection under model uncertainty. In addition, many existing models such as LASSO...

Building on the testable implications for IV validity underlying local average treatment effect (LATE) estimation, we (i) propose a simple testing procedure that may accommodate high-dimensional covariates and (ii) demonstrate that it can also detect biases arising from misspecified IV...

This paper presents a method to improve existing spatial dependence-robust inference procedures for spatial data. Our method involves augmenting a regression specification with functions of locations that reduce spatial correlation in regression scores and serves as a pre-whitener, followed by...