Nikolas Kuschnig

Nikolas Kuschnig
Wirtschaftsuniversität Wien | WU · Department of Economics

MSc

About

12
Publications
6,105
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86
Citations
Citations since 2016
12 Research Items
86 Citations
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Publications

Publications (12)
Preprint
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The sensitivity of econometric results is central to their credibility. In this paper, we investigate the sensitivity of regression-based inference to influential sets of observations and show how to reliably identify and interpret them. We explore three algorithmic approaches to analyze influential sets, and assess the sensitivity of a number of e...
Article
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In a recent study, Chaves et al. find international consumption and trade to be major drivers of ‘malaria risk’ via deforestation. Their analysis is based on a counterfactual ‘malaria risk’ footprint, defined as the number of malaria cases in absence of two malaria interventions, which is constructed using linear regression. In this letter, I argue...
Article
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Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Spillover effects and factors that differ across locations and over time play important roles in these processes. They are lar...
Article
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Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods are often employed to deal with their dense parameterization, imposing structure on model coefficients via prior information. The optimal choice of the degree of informativeness implied by these p...
Article
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Bayesian approaches play an important role in the development of new spatial econometric methods, but are uncommon in applied work. This is partly due to a lack of accessible, flexible software for the Bayesian estimation of spatial models. Established probabilistic software struggles with the specifics of spatial econometrics, while classical impl...
Article
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Growing demand for minerals continues to drive deforestation worldwide. Tropical forests are particularly vulnerable to the environmental impacts of mining and mineral processing. Many local- to regional-scale studies document extensive, long-lasting impacts of mining on biodiversity and ecosystem services. However, the full scope of deforestation...
Preprint
Full-text available
Bayesian approaches to spatial econometric models are relatively uncommon in applied work, but play an important role in the development of new methods. This is partly due to a lack of easily accessible, flexible software for the Bayesian estimation of spatial models. Established probabilistic software struggles with computational specifics of thes...
Preprint
Full-text available
Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Indirect impacts and determinants that eventually differ across locations and over time are important factors in these process...
Preprint
Full-text available
In their recent study, Chaves et al. (1) investigate the role of international trade and consumption as a driver of malaria risk via deforestation. They base their analysis on a malaria risk footprint, which they define as a counterfactual. The measure is constructed using a linear regression model, which is used to capture causal linkages. Chaves...
Preprint
Full-text available
Vector autoregression (VAR) models are widely used models for multivariate time series analysis, but often suffer from their dense parameterization. Bayesian methods are commonly employed as a remedy by imposing shrinkage on the model coefficients via informative priors, thereby reducing parameter uncertainty. The subjective choice of the informati...
Article
Full-text available
Harvested biomass is linked to final consumption by networks of processes and actors that convert and distribute food and non-food goods. Achieving a sustainable resource metabolism of the economy is an overarching challenge which manifests itself in a number of the UN Sustainable Development Goals. Modelling the physical dimensions of biomass conv...

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Project (1)
Project
The objective of FINEPRINT is to advance current footprint models through developing new methods for assessing global material flows using high geographical resolution. With these new approaches, we perform spatially explicit analyses of worldwide raw material flows embodied in products and services and assess the associated environmental impacts around the globe, including issues such as deforestation and water scarcity. FINEPRINT is funded by the European Research Council (ERC). For more information about the FINEPRINT project, see www.fineprint.global. Also visit our open science portal on GitHub: github.com/fineprint-global.