Long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / Siti NurulAifa Mohd ZainulAbidin
Oil plays an essential factor in Malaysia's economic growth. Malaysia is the second largest oil producer in South East Asia and has the 24th largest crude oil reserves. Considering that oil is a depleting and a highly demanded global commodity, fluctuations in its prices may cause an impact on...
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my.uitm.ir.619362022-07-24T00:12:02Z https://ir.uitm.edu.my/id/eprint/61936/ Long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / Siti NurulAifa Mohd ZainulAbidin Mohd ZainulAbidin, Siti NurulAifa Commodities. Commercial products. Generic products Oil plays an essential factor in Malaysia's economic growth. Malaysia is the second largest oil producer in South East Asia and has the 24th largest crude oil reserves. Considering that oil is a depleting and a highly demanded global commodity, fluctuations in its prices may cause an impact on several macroeconomic variables such as gross domestic product (GDP), inflation rates and the production. It is essential to know the relationship between crude oil price and the macroeconomic variables to determine the economic interdependence. The main purpose of this study is to identify the causal relationship between crude oil price and the macroeconomic variables using vector autoregressive (VAR) model approach. This study utilized data from the year 2006 to2016 on a monthly basis. The result of a VAR model approach indicates that GDP and inflation are the significant variables that will affect crude oil price. In addition, the study made a forecast to all the four variables by using geometric Brownian motion (GBM)model. Hence, the crucial part in this study is to determine which method is more reliable in forecasting crude oil price. This is done by comparing the mean absolute percentage error (MAPE) of VAR model approach and GBM. The lowest MAPE value indicates the highest accuracy of the forecast values. In this study, for both methods MAPE were below twenty percent, however, MAPE for VAR model approach is the lowest, which is3.84 compared with 20.48. Thus, the VAR model approach is used to forecast crude oil price for the year 2018 on a monthly basis. 2018-01 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/61936/1/61936.PDF Long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / Siti NurulAifa Mohd ZainulAbidin. (2018) Masters thesis, thesis, Universiti Teknologi MARA. |
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Commodities. Commercial products. Generic products Mohd ZainulAbidin, Siti NurulAifa Long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / Siti NurulAifa Mohd ZainulAbidin |
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Oil plays an essential factor in Malaysia's economic growth. Malaysia is the second largest oil producer in South East Asia and has the 24th largest crude oil reserves. Considering that oil is a depleting and a highly demanded global commodity, fluctuations in its prices may cause an impact on several macroeconomic variables such as gross domestic product (GDP), inflation rates and the production. It is essential to know the relationship between crude oil price and the macroeconomic variables to determine the economic interdependence. The main purpose of this study is to identify the causal relationship between crude oil price and the macroeconomic variables using vector autoregressive (VAR) model approach. This study utilized data from the year 2006 to2016 on a monthly basis. The result of a VAR model approach indicates that GDP and inflation are the significant variables that will affect crude oil price. In addition, the study made a forecast to all the four variables by using geometric Brownian motion (GBM)model. Hence, the crucial part in this study is to determine which method is more reliable in forecasting crude oil price. This is done by comparing the mean absolute percentage error (MAPE) of VAR model approach and GBM. The lowest MAPE value indicates the highest accuracy of the forecast values. In this study, for both methods MAPE were below twenty percent, however, MAPE for VAR model approach is the lowest, which is3.84 compared with 20.48. Thus, the VAR model approach is used to forecast crude oil price for the year 2018 on a monthly basis. |
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Thesis |
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Mohd ZainulAbidin, Siti NurulAifa |
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Mohd ZainulAbidin, Siti NurulAifa |
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Mohd ZainulAbidin, Siti NurulAifa |
title |
Long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / Siti NurulAifa Mohd ZainulAbidin |
title_short |
Long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / Siti NurulAifa Mohd ZainulAbidin |
title_full |
Long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / Siti NurulAifa Mohd ZainulAbidin |
title_fullStr |
Long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / Siti NurulAifa Mohd ZainulAbidin |
title_full_unstemmed |
Long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / Siti NurulAifa Mohd ZainulAbidin |
title_sort |
long-run dynamics of crude oil price and macroeconomic variables: a cointegration vector autoregressive analysis / siti nurulaifa mohd zainulabidin |
publishDate |
2018 |
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https://ir.uitm.edu.my/id/eprint/61936/1/61936.PDF https://ir.uitm.edu.my/id/eprint/61936/ |
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1739834078722523136 |
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13.19449 |