ArticleResources policy2022
Analyzing the nexus of COVID-19 and natural resources and commodities: Evidence from time-varying causality.
Article in Resources policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Time-varying effects of fuel prices on stock market returns during COVID-19 outbreak.Resources policy · 2023Article
- Impact of the Global Fear Index (COVID-19 Panic) on the S&P Global Indices Associated with Natural Resources, Agribusiness, Energy, Metals, and Mining: Granger Causality and Shannon and Rényi Transfer Entropy.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Even though a few studies have focused on natural resources and commodity sectors by considering the pandemic, they have only compared their status in pre-COVID19 to post-COVID19. None of the studies has directly examined the causal relationship between the pandemic, and natural resource index and the primary commodity-related sector indices. This study fills the gap of exploring the dynamic association between them by analyzing the causal relationship between the COVID19, and natural resources index and the primary commodity-related sectors (i.e., agribusiness, energy, and metals & mining) by applying a novel time-varying causality test on daily data from January 23, 2020, to November 12, 2021. The empirical results support the presence of time-varying causality from COVID19 to natural resources, agribusiness, energy and metals & mining. The results obtained from the rolling window algorithm support causal linkages between the variables however at several points it fails to capture the dynamics of linkages between the variables which is captured by the recursive window algorithm. The outcome is robust when the pandemic is proxied by either number of cases or deaths. Similarly, the findings obtained from heteroskedastic-robust specification also validate our findings. Several policy implications are further discussed in the study.
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