Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model
Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index pr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
UTS ePRESS
2018-03-01
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| Series: | Construction Economics and Building |
| Subjects: | |
| Online Access: | https://learning-analytics.info/journals/index.php/AJCEB/article/view/5604 |
| _version_ | 1811222858636984320 |
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| author | Ernest Kissi Theophilus Adjei-Kumi Peter Amoah Jerry Gyimah |
| author_facet | Ernest Kissi Theophilus Adjei-Kumi Peter Amoah Jerry Gyimah |
| author_sort | Ernest Kissi |
| collection | DOAJ |
| description | Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI) is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX) in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics. |
| first_indexed | 2024-04-12T08:22:57Z |
| format | Article |
| id | doaj.art-4aa24d1b7fd148e59e6ed20370d29888 |
| institution | Directory Open Access Journal |
| issn | 2204-9029 |
| language | English |
| last_indexed | 2024-04-12T08:22:57Z |
| publishDate | 2018-03-01 |
| publisher | UTS ePRESS |
| record_format | Article |
| series | Construction Economics and Building |
| spelling | doaj.art-4aa24d1b7fd148e59e6ed20370d298882022-12-22T03:40:30ZengUTS ePRESSConstruction Economics and Building2204-90292018-03-0118110.5130/AJCEB.v18i1.56043484Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables ModelErnest Kissi0Theophilus Adjei-Kumi1Peter Amoah2Jerry Gyimah3Department of Building Technology, Kwame Nkrumah University of Science and TechnologyDepartment of Building Technology, Kwame Nkrumah University of Science and TechnologyDepartment of Building Technology, Kwame Nkrumah University of Science and TechnologyBuilding and Road Research InstitutePrices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI) is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX) in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.https://learning-analytics.info/journals/index.php/AJCEB/article/view/5604Forecastingtender price indexARIMAXGhana |
| spellingShingle | Ernest Kissi Theophilus Adjei-Kumi Peter Amoah Jerry Gyimah Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model Construction Economics and Building Forecasting tender price index ARIMAX Ghana |
| title | Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model |
| title_full | Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model |
| title_fullStr | Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model |
| title_full_unstemmed | Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model |
| title_short | Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model |
| title_sort | forecasting construction tender price index in ghana using autoregressive integrated moving average with exogenous variables model |
| topic | Forecasting tender price index ARIMAX Ghana |
| url | https://learning-analytics.info/journals/index.php/AJCEB/article/view/5604 |
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