A time-series analysis of UK annual and quarterly construction output data (1955-1995)

Andrew Agapiou, David Notman, Roger Flanagan, George Norman

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

United Kingdom construction output makes a vitally important contribution to the gross domestic product of the UK economy. Nevertheless, despite the obvious importance of UK construction, very little is known about the output behaviour of the industry. This paper endeavours to redress the imbalance by analysing the post-war time-series behaviour of annual and quarterly UK construction output. The primary technique of analysis is to estimate an autoregressive integrated moving-average (ARIMA) model of UK construction output. Such a model proxies the stochastic or random process that underlies UK construction output data. Also, a review is given of the methodology of estimation and diagnostic checking of ARIMA models in the context of UK construction output, together with ex-post and ex-ante forecasts of UK construction output using the estimated ARIMA models.
Original languageEnglish
Pages (from-to)409-416
Number of pages8
JournalConstruction Management and Economics
Volume16
Issue number4
DOIs
Publication statusPublished - 1998

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Time series analysis
Random processes
Time series
Industry

Keywords

  • time series data
  • Arima models
  • random processes
  • architecture
  • construction industry

Cite this

Agapiou, Andrew ; Notman, David ; Flanagan, Roger ; Norman, George. / A time-series analysis of UK annual and quarterly construction output data (1955-1995). In: Construction Management and Economics. 1998 ; Vol. 16, No. 4. pp. 409-416.
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A time-series analysis of UK annual and quarterly construction output data (1955-1995). / Agapiou, Andrew; Notman, David; Flanagan, Roger; Norman, George.

In: Construction Management and Economics, Vol. 16, No. 4, 1998, p. 409-416.

Research output: Contribution to journalArticle

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AU - Flanagan, Roger

AU - Norman, George

PY - 1998

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N2 - United Kingdom construction output makes a vitally important contribution to the gross domestic product of the UK economy. Nevertheless, despite the obvious importance of UK construction, very little is known about the output behaviour of the industry. This paper endeavours to redress the imbalance by analysing the post-war time-series behaviour of annual and quarterly UK construction output. The primary technique of analysis is to estimate an autoregressive integrated moving-average (ARIMA) model of UK construction output. Such a model proxies the stochastic or random process that underlies UK construction output data. Also, a review is given of the methodology of estimation and diagnostic checking of ARIMA models in the context of UK construction output, together with ex-post and ex-ante forecasts of UK construction output using the estimated ARIMA models.

AB - United Kingdom construction output makes a vitally important contribution to the gross domestic product of the UK economy. Nevertheless, despite the obvious importance of UK construction, very little is known about the output behaviour of the industry. This paper endeavours to redress the imbalance by analysing the post-war time-series behaviour of annual and quarterly UK construction output. The primary technique of analysis is to estimate an autoregressive integrated moving-average (ARIMA) model of UK construction output. Such a model proxies the stochastic or random process that underlies UK construction output data. Also, a review is given of the methodology of estimation and diagnostic checking of ARIMA models in the context of UK construction output, together with ex-post and ex-ante forecasts of UK construction output using the estimated ARIMA models.

KW - time series data

KW - Arima models

KW - random processes

KW - architecture

KW - construction industry

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