Mao, Z (2003) Forecasting total factor productivity growth in the construction industry using neural network modelling. PhD thesis, National University of Singapore, Singapore.
Abstract
Total factor productivity (TFP) is a comprehensive industry-level productivity measure and determines an industry's competitiveness. This research proposes Jorgenson's method as an appropriate TFP measurement for the construction industry. It is then applied to estimate TFP growth in Singapore's construction industry. It is found that TFP growth tends to move in tandem with the construction business cycle. As a monitor of progress towards TFP growth, firstly factors affecting TFP growth of the construction industry of Singapore are identified and significant indicators are selected. Secondly, models using alternative techniques for forecasting TFP growth are developed and compared. As factors influencing TFP growth are complicatedly interacted, Artificial Neural networks (ANNs) are applied to solve such complex nonlinear mappings. To overcome overfitting caused by small dataset, Bayesian Neural Network (BNN) is adopted. The result shows that ANNs can model TFP growth more accurately than the regression technique. Finally, several policy implications are made.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Hua, G B and Shouqing, W |
| Uncontrolled Keywords: | competitiveness; measurement; forecasting; policy; productivity; total factor productivity; artificial neural network; Singapore; neural network |
| Index terms: | mapping, dataset, neural network, total factor productivity, artificial neural network, competitiveness, construction industry, estimate, modelling, policy implication, Singapore, forecasting, productivity |
| Subjects: | policy studies, production economics, modelling and simulation, analytical methods, financial and cost management, prediction and forecasting, artificial intelligence, spatial and geospatial analysis, management, Geography, market analysis, data management, industry analysis |
| Topics: | Geographical Context, Engineering Principles, Cost Management, Business Strategy, Research Practice, Governance, Digital Applications |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here