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....

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...

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...

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...

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...

"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...

We develop an “omitted variable bias” framework for sensitivity analysis of instrumental variable (IV) estimates that is immune to “weak instruments,” naturally handles multiple “side-effects” (violations of the exclusion restriction assumption) and “confounders” (violations of...

The study aims to present from a technical-economic point of view aspects related to the cost behaviour, according to the cost-volume-profit analysis model and the sensitivity analysis, of cereal products (maize and wheat) grown in conventional and organic farming systems. In Romania, according...

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose...

This study aims to explore combining several solar photovoltaic energy resources-diesel generators and batteries as a hybrid power system technically and economically to compensate for the lack of required power consumption due to the outages in the national power distribution grid. At first, a...

We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the distribution of the input factors as well as...

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model,...

This paper presents a methodology which blends sensitivity analysis and fuzzy arithmetic for managing uncertainty in project financing transactions. Specifically, we adopt the perspective of the equityholders and use the average Return On Equity (ROE) to measure shareholder value creation and,...

In this work we present a new framework for modelling portfolio dynamics and how to incorporate this information in the portfolio selection process. We define drivers for asset and portfolio dynamics, and their optimal selection. We introduce the new Commonality Principle, which gives a solution...