Predicting gasoline prices using Michigan survey data

This study investigates the predictive power of Michigan Surveys of Consumers (MSC) data for gasoline prices. Specifically, we utilize the MSC data on both expected inflation and consumer sentiment to construct a vector autoregressive (VAR) model for forecasting gasoline prices for 2003-2014. Our fi...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي: Baghestani, Hamid (author)
التنسيق: article
منشور في: 2015
الموضوعات:
الوصول للمادة أونلاين:http://hdl.handle.net/11073/8169
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author Baghestani, Hamid
author_facet Baghestani, Hamid
author_role author
dc.creator.none.fl_str_mv Baghestani, Hamid
dc.date.none.fl_str_mv 2015-07
2016-03-01T09:22:22Z
2016-03-01T09:22:22Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv Baghestani, Hamid. "Predicting gasoline prices using Michigan survey data." Energy Economics 50, no. 4 (July, 2015): 27-32.
0140-9883
http://hdl.handle.net/11073/8169
10.1016/j.eneco.2015.04.015
dc.language.none.fl_str_mv en_US
dc.relation.none.fl_str_mv http://www.sciencedirect.com/science/article/pii/S0140988315001383
dc.subject.none.fl_str_mv Energy prices
Expected inflation
Consumer sentiment
Forecast accuracy
dc.title.none.fl_str_mv Predicting gasoline prices using Michigan survey data
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description This study investigates the predictive power of Michigan Surveys of Consumers (MSC) data for gasoline prices. Specifically, we utilize the MSC data on both expected inflation and consumer sentiment to construct a vector autoregressive (VAR) model for forecasting gasoline prices for 2003-2014. Our findings indicate that the VAR forecasts are superior to the comparable benchmark forecasts obtained from a univariate integrated moving average (MA) model in terms of both predictive information content and directional accuracy. As such, we conclude that the MSC data on both expected inflation and consumer sentiment have significant predictive information for gasoline prices. Further inspection reveals that the VAR forecasts are particularly accurate for the period since 2008, reinforcing the notion that consumers are "economically" rational.
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identifier_str_mv Baghestani, Hamid. "Predicting gasoline prices using Michigan survey data." Energy Economics 50, no. 4 (July, 2015): 27-32.
0140-9883
10.1016/j.eneco.2015.04.015
language_invalid_str_mv en_US
network_acronym_str aus
network_name_str aus
oai_identifier_str oai:repository.aus.edu:11073/8169
publishDate 2015
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
spelling Predicting gasoline prices using Michigan survey dataBaghestani, HamidEnergy pricesExpected inflationConsumer sentimentForecast accuracyThis study investigates the predictive power of Michigan Surveys of Consumers (MSC) data for gasoline prices. Specifically, we utilize the MSC data on both expected inflation and consumer sentiment to construct a vector autoregressive (VAR) model for forecasting gasoline prices for 2003-2014. Our findings indicate that the VAR forecasts are superior to the comparable benchmark forecasts obtained from a univariate integrated moving average (MA) model in terms of both predictive information content and directional accuracy. As such, we conclude that the MSC data on both expected inflation and consumer sentiment have significant predictive information for gasoline prices. Further inspection reveals that the VAR forecasts are particularly accurate for the period since 2008, reinforcing the notion that consumers are "economically" rational.2016-03-01T09:22:22Z2016-03-01T09:22:22Z2015-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfBaghestani, Hamid. "Predicting gasoline prices using Michigan survey data." Energy Economics 50, no. 4 (July, 2015): 27-32.0140-9883http://hdl.handle.net/11073/816910.1016/j.eneco.2015.04.015en_UShttp://www.sciencedirect.com/science/article/pii/S0140988315001383oai:repository.aus.edu:11073/81692024-08-22T12:18:04Z
spellingShingle Predicting gasoline prices using Michigan survey data
Baghestani, Hamid
Energy prices
Expected inflation
Consumer sentiment
Forecast accuracy
status_str publishedVersion
title Predicting gasoline prices using Michigan survey data
title_full Predicting gasoline prices using Michigan survey data
title_fullStr Predicting gasoline prices using Michigan survey data
title_full_unstemmed Predicting gasoline prices using Michigan survey data
title_short Predicting gasoline prices using Michigan survey data
title_sort Predicting gasoline prices using Michigan survey data
topic Energy prices
Expected inflation
Consumer sentiment
Forecast accuracy
url http://hdl.handle.net/11073/8169