Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about...

Prevailing methods for measuring sensitive outcomes confront researchers with an inherent bias-variance trade-off: direct questioning is prone to a sensitivity bias, while indirect methods such as list experiments are substantially less precise. We introduce the ballot-bag, a novel technique...

This study aims to design and optimize a Photovoltaic/Diesel/Battery hybrid energy system to provide a stable and sustainable electricity supply for the University Institute of Technology Fotso Victor in Bandjoun, Cameroon, which currently suffers from frequent power outages. The main objective...

Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on the conditional parallel trends assumption: In the absence of treatment, the average outcome of the treated and...

Aim: The study examined the behavior of exchange rate in ASEAN countries. This was highly necessitated in order to account for the structural break in the data set occasioned by global financial crisis. Research method: The quantile regression sensitivity analysis was performed on daily series...

We critically assess an almost universal Benefit-Cost Analysis (BCA) practice. In addition to the central Net Present Value (NPV), analysts frequently also report multiple additional values in what is commonly referred to as ‘NPV sensitivity analysis’. This practice is generally justified...

Ambuehl et al. (2022) explore ways to evaluate interventions designed to enhance decision-making quality when individuals misjudge the outcomes of their choices. The authors propose a novel outcome metric that can distinguish between interventions better than conventional metrics such as...

Vaccine boosters have been recommended to mitigate the spread of the coronavirus disease 2019 (COVID-19) pandemic. A mathematical model with three vaccine doses and susceptibility is formulated. The model is calibrated using the cumulative number of hospitalized cases from Alberta, Canada....

Building on a recently developed methodology for sensitivity analysis that parametrizes omitted variable bias in terms of partial R2 measures, I propose a simple statistic to capture the severity of omitted variable bias in any observational study: the probability of omitted variable bias...

The term "high-risk population" refers to individuals with an increased likelihood of contracting a severe illness or disease due to factors such as age, underlying medical conditions, pregnancy, geographical location, or a combination of these factors. This paper proposes a mathematical model...

Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking under the...

A popular methodology for sensitivity analysis offers two concrete proposal to draw conclusions about the severity of omitted variable bias: (a) comparing delta* (the value of the sensitivity parameter, delta, consistent with zero treatment effect) with 1, and (b) constructing identified sets...

Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking after...

A multi-period inventory management model for perishables is developed using a simulation approach. Single-product scenario and periodic review policy are assumed, considering uniform demand, non-null deterministic lead time, fixed shelf life and service level constraints.Simulation is used to...

"SimDec is a revolution in decision-making support. SimDec "teases out" inherent cause-and-effect relationships and reveals the intricacy of relationships between sets of input and output variables. At its core, SimDec is an amalgamation of uncertainty and global sensitivity analysis with an...

"SimDec is a revolution in decision-making support. SimDec "teases out" inherent cause-and-effect relationships and reveals the intricacy of relationships between sets of input and output variables. At its core, SimDec is an amalgamation of uncertainty and global sensitivity analysis with an...