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Construction demand modeling: A systematic approach to using economic indicators and a comparative study of alternative forecasting approaches

机译：施工需求建模：使用经济指标的系统方法和替代预测方法的比较研究

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摘要

The published literature abounds with evidence of a close relationship between the construction industry and the national economy. This study reinforces the strength of this relationship by proposing the use of economic indicators to model demand for construction. Alternative forecasting approaches are applied, comprising both traditional and state-of-the-art techniques. The aim is to establish the most theoretically significant and statistically adequate indicators, and the most accurate forecasting technique for modelling and predicting construction demand. A systematic approach is proposed to identify and select economic indicators that relate to demand for construction. It involves four distinct stages and they are: (1) theoretical identification: (2) data collection and pre-processing; (3) statistical selection; and (4) usage. This stage-by-stage process is illustrated on residential, industrial and commercial-type construction in Singapore. The findings confirm that demand in the construction industry is significantly related to a wide range of economic measures. A comparative study of regression and non-regression approaches of forecasting is earned out using Singapore's residential sector as a case-study. The techniques include the Multiple Linear Regression, the Multiple Log-linear Regression, the Autoregressive Non-linear Regression Algorithm and the Artificial Neural Network (ANN). Seven economic indicators have been selected to build the demand models, and they are: Building tender price index; Bank lending for housing; Population size; Housing stock (additions); National savings; Gross fixed capital formation for residential buildings; and Unemployment rate. Quarterly time-series data over the period 1975 - 1994 are used. Several conclusions are drawn. Firstly, non-linear methods produce more accurate forecasts. Secondly, the Multiple Log-linear is the most accurate regression technique. Thirdly, the ANN technique, a non-regression approach, performs outstandingly better than the regression approach. Keywords: Demand, economic indicators, forecasting, regression, artificial neural network.
机译：出版的文献充斥着建筑业与国民经济之间密切关系的证据。这项研究通过建议使用经济指标来模拟建筑需求来加强这种关系的强度。应用了包括传统技术和最新技术在内的替代性预测方法。目的是建立理论上最重要，统计上最适当的指标，以及用于建模和预测建筑需求的最准确的预测技术。提出了一种系统的方法来识别和选择与建筑需求有关的经济指标。它涉及四个不同的阶段，它们是：（1）理论鉴定：（2）数据收集和预处理； （3）统计选择； （4）用法。在新加坡的住宅，工业和商业类型建筑中，已逐步说明了这一逐步过程。调查结果证实，建筑行业的需求与广泛的经济措施有很大关系。以新加坡的住宅部门为案例研究，进行了回归和非回归预测方法的比较研究。这些技术包括多元线性回归，多元对数线性回归，自回归非线性回归算法和人工神经网络（ANN）。选择了七个经济指标来建立需求模型，它们是：建立投标价格指数；银行住房贷款；人口规模；房屋存量（增加）；国家储蓄；住宅固定资本形成总额；和失业率。使用了1975年至1994年期间的季度时间序列数据。得出了一些结论。首先，非线性方法可以产生更准确的预测。其次，Multiple Log-linear是最准确的回归技术。第三，ANN技术（一种非回归方法）的性能要比回归方法出色。关键字：需求，经济指标，预测，回归，人工神经网络。

著录项

  • 作者

    Goh, Bee Hua.;

  • 作者单位

    University of London, University College London (United Kingdom).;

  • 授予单位 University of London, University College London (United Kingdom).;
  • 学科 Economics.
  • 学位 Ph.D.
  • 年度 1997
  • 页码 281 p.
  • 总页数 281
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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