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A Bayesian panel VAR model to analyze the impact of climate change on high-income economies

Florian Huber, Tamás Krisztin, Michael Pfarrhofer

arXiv 4 Apr 2018 · Econometrics

arXiv:1804.01554 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In this paper, we assess the impact of climate shocks on futures markets for agricultural commodities and a set of macroeconomic quantities for multiple high-income economies. To capture relations among countries, markets, and climate shocks, this paper proposes parsimonious methods to estimate high-dimensional panel VARs. We assume that coefficients associated with domestic lagged endogenous variables arise from a Gaussian mixture model while further parsimony is achieved using suitable global-local shrinkage priors on several regions of the parameter space. Our results point towards pronounced global reactions of key macroeconomic quantities to climate shocks. Moreover, the empirical findings highlight substantial linkages between regionally located climate shifts and global commodity markets.

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73
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Nazlioglu S, and Soytas U (2011) World oil prices and agricultural commodity prices: Evidence from an emerging market1.00083100%
2Malsiner-Walli G, Frühwirth-Schnatter S, and Grün B (2016) Model-based clustering based on sparse finite Gaussian mixtures1.00073100%
3Lucotte Y (2016) Co-movements between crude oil and food prices: A post-commodity boom perspective1.00053100%
4Kastner G (2019) a), Sparse Bayesian time-varying covariance estimation in many dimensions0.9285380%
5Akram QF (2009) Commodity prices, interest rates and the dollar0.92843100%
6Headey D (2011) Rethinking the global food crisis: The role of trade shocks0.87472100%
7Huber F, and Feldkircher M (2019) Adaptive shrinkage in Bayesian vector autoregressive models0.8434375%
8Koop G, and Korobilis D (2016) Model uncertainty in panel vector autoregressive models0.81142100%
9Nazlioglu S (2011) World oil and agricultural commodity prices: Evidence from nonlinear causality0.73732100%
10Canova F, and Ciccarelli M (2009) Estimating multicountry VAR models0.73732100%

Showing the top 10 of 73 scored citations.