Forecasting Natural Gas Prices in Real Time

Christiane Baumeister, Florian Huber, Thomas K. Lee and Francesco Ravazzolo

No 19669, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.

JEL-codes: C11 C32 C52 Q41 Q47 (search for similar items in EconPapers)
Date: 2024-11
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP19669 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:19669

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP19669

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().