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Commodity prices and inflation risk (replication data) - Dataset - B2FIND
- b2find.dkrz.de
Updated Oct 24, 2023
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EmailClick to copy linkLink copiedCite(2023). Commodity prices and inflation risk (replication data) - Dataset - B2FIND [Dataset]. https://b2find.dkrz.de/dataset/cfa19df9-1cc1-5610-923c-dd27e0cf2407Dataset updatedOct 24, 2023DescriptionThis paper investigates the role of commodity price information when evaluating inflation risk. Using a model averaging approach, we provide strong evidence of in-sample and out-of-sample predictive ability from commodity prices and convenience yields to inflation, establishing clear point and density forecast performance gains when incorporating disaggregated commodities price information. The resulting forecast densities are used to calculate the (ex-ante) risk of inflation breaching defined thresholds that broadly characterize periods of high and low inflation. We find that information in commodity prices significantly enhances our ability to pick out tail inflation events and to characterize the level of risks associated with periods of high volatility in commodity prices.
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Commodity prices and inflation risk (replication data)
- journaldata.zbw.eu
- jda-test.zbw.eu
csv, txtUpdated Feb 20, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteAnthony Garratt; Ivan Petrella; Anthony Garratt; Ivan Petrella (2024). Commodity prices and inflation risk (replication data) [Dataset]. http://doi.org/10.15456/jae.2022327.072116csv(107794), txt(3627), csv(9159), csv(110118), csv(4873), csv(109621), csv(4523), csv(8810), csv(6510)Available download formatsUnique identifierhttps://doi.org/10.15456/jae.2022327.072116Dataset updatedFeb 20, 2024Dataset provided byZBW - Leibniz Informationszentrum WirtschaftAuthorsAnthony Garratt; Ivan Petrella; Anthony Garratt; Ivan PetrellaLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThis paper investigates the role of commodity price information when evaluating inflation risk. Using a model averaging approach, we provide strong evidence of in-sample and out-of-sample predictive ability from commodity prices and convenience yields to inflation, establishing clear point and density forecast performance gains when incorporating disaggregated commodities price information. The resulting forecast densities are used to calculate the (ex-ante) risk of inflation breaching defined thresholds that broadly characterize periods of high and low inflation. We find that information in commodity prices significantly enhances our ability to pick out tail inflation events and to characterize the level of risks associated with periods of high volatility in commodity prices.
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FacebookTwitterEmailThis paper investigates the role of commodity price information when evaluating inflation risk. Using a model averaging approach, we provide strong evidence of in-sample and out-of-sample predictive ability from commodity prices and convenience yields to inflation, establishing clear point and density forecast performance gains when incorporating disaggregated commodities price information. The resulting forecast densities are used to calculate the (ex-ante) risk of inflation breaching defined thresholds that broadly characterize periods of high and low inflation. We find that information in commodity prices significantly enhances our ability to pick out tail inflation events and to characterize the level of risks associated with periods of high volatility in commodity prices.