Forecasting nuclear power supply with Bayesian autoregression

Roderick Beck; John L. Solow

doi:10.1016/0140-9883(94)90032-9

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Forecasting nuclear power supply with Bayesian autoregression

Journal article

Peer reviewed

Forecasting nuclear power supply with Bayesian autoregression

Roderick Beck and John L. Solow

Energy economics, Vol.16(3), pp.185-192

07/01/1994

DOI: 10.1016/0140-9883(94)90032-9

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Abstract

We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. © 1994.

Bayesian models

Forecasting

Nuclear power

Details

Title: Subtitle: Forecasting nuclear power supply with Bayesian autoregression
Creators: Roderick Beck - University of Iowa
John L. Solow - University of Iowa
Resource Type: Journal article
Publication Details: Energy economics, Vol.16(3), pp.185-192
DOI: 10.1016/0140-9883(94)90032-9
ISSN: 0140-9883
eISSN: 1873-6181
Number of pages: 8
Language: English
Date published: 07/01/1994
Academic Unit: Economics
Record Identifier: 9984963153102771

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