Modelling and forecasting monthly petroleum prices of Ghana using subset ARIMA models - Softcover

Okyere, Francis

 
9783656483656: Modelling and forecasting monthly petroleum prices of Ghana using subset ARIMA models
Softcover
ISBN 10: 3656483655 ISBN 13: 9783656483656
Verlag: GRIN Verlag, 2013

Inhaltsangabe

Bachelor Thesis from the year 2012 in the subject Economics - Statistics and Methods, grade: none, , language: English, abstract: The study is an attempt to build a univariate Time Series Model to forecast monthly petroleum prices for 2010/2011, from January 1990 to September 2010, since national petroleum agency (NPA) is failing to plan for fluctuation of petroleum prices. The data was source from the website of Bank of Ghana. The study employs Box-Jenkins methodology of building Seasonal Autoregressive Integrated Moving Average (SARIMA) model to achieve various objectives. Different selected models were tested by Residual plots of Autocorrelation and Partial Autocorrelation and Ljung Box Q statistic to ensure adequacy of results. The results reveal that demand and supply, crudel oil prices, gasoline, natural disasters and government regulations are some of factors that can influence fuel prices and ARIMA(1,1,5)×(1,0,1)11 is the best model for forecast. The future values expose that during the months to come; petroleum prices are going to experience an insignificant increase. In light of the forecast, I know Ghana will ascertain a healthy state of economy.

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Reseña del editor

Bachelor Thesis from the year 2012 in the subject Economics - Statistics and Methods, grade: none, , language: English, abstract: The study is an attempt to build a univariate Time Series Model to forecast monthly petroleum prices for 2010/2011, from January 1990 to September 2010, since national petroleum agency (NPA) is failing to plan for fluctuation of petroleum prices. The data was source from the website of Bank of Ghana. The study employs Box-Jenkins methodology of building Seasonal Autoregressive Integrated Moving Average (SARIMA) model to achieve various objectives. Different selected models were tested by Residual plots of Autocorrelation and Partial Autocorrelation and Ljung Box Q statistic to ensure adequacy of results. The results reveal that demand and supply, crudel oil prices, gasoline, natural disasters and government regulations are some of factors that can influence fuel prices and ARIMA(1,1,5)×(1,0,1)11 is the best model for forecast. The future values expose that during the months to come; petroleum prices are going to experience an insignificant increase. In light of the forecast, I know Ghana will ascertain a healthy state of economy.

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Verlag
GRIN Verlag
Erscheinungsdatum
2013
Sprache
Englisch
ISBN 10 
3656483655
ISBN 13 
9783656483656
Einband
Tapa blanda
Auflage
1
Anzahl der Seiten
110
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar