An integrated forecasting system for the St. John's Census Metropolitan Area, Newfoundland

Deng, William (Wenxiong) (1996) An integrated forecasting system for the St. John's Census Metropolitan Area, Newfoundland. Masters thesis, Memorial University of Newfoundland.

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    Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
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Abstract

The objective of this thesis is to construct and evaluate a regional Integrated Forecasting System (IFS) using the St John's, Newfoundland, Census Metropolitan Area (CMA) as a case study area. The purpose of an IFS is to model the relationships among a number of key variables (e.g. population, employment and housing) which are central to the planning process for any area. Demographic characteristics, for example, are important factors in the need or ability to attract employment and the demand and supply of housing. At the same time, new employment opportunities may result in in-migration or reduce out-migration. Furthermore, lack of available housing may act as a constraint on in-migration, or new house building may encourage it. These inter-linkages require the development of a forecasting approach which is characterized by common and compatible supply-demand assumptions for the key variables and an explicit consideration of the relationships among them. The product from this thesis is such a forecasting model. To demonstrate the capabilities of the IFS, the potential impacts of the Hibernia offshore oil project on selected socio-economic activities in the St. John's Census Metropolitan Area (CMA) are examined as a case study. -- Chapters I and II of the thesis provide a background to the work, describing the St. John's CMA study area, the recent economic development history of the St. John's region and Newfoundland, and reviewing the literature on integrated forecasting systems. -- The structure and evaluation of the model and data are described in Chapter III. The overall design of the IFS follows the basic format of models developed for the Gloucestershire and Grampian regions in the United Kingdom. The IFS is basically a recursive rather than a simultaneous model. This model consists of five interacting blocks: labour demand, labour supply, migration and household formation, housing demand and housing supply. A series of tests are applied to each regression equation (R2. standard error of estimate statistics, F-test, t- statistics and Durbin-Watson statistics) developed to describe each block and, where necessary, modifications are made to the equations in order to improve the model's performance. The sources and methods used in the preparation of the data input are discussed in the last section of this chapter. -- In Chapter IV, the characteristics of the equations and the within-sample performance of the model are evaluated. Mean Absolute Percentage Error (MAPE) statistics are calculated to examine the model's performance. This evaluation IFS suggests that the model is appropriate for projecting the impacts of the Hibernia Project on the socio-economic characteristics in the St. John's CMA and has the potential to project impacts for other major projects or sectoral changes. -- Chapter V describes the model's forecasting performance and the assumptions on which the IFS is based. According to the EFS, the Hibernia Project will not change the basic economic situation of the St. John's CMA — St. John's will not become another Calgary in eastern Canada — though it will help the economy of the region to some extent. Overall, this thesis concludes that the IFS provides valuable which can be of value to both regional planners and local businesses.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/733
Item ID: 733
Additional Information: Bibliography: leaves 168-173.
Department(s): Humanities and Social Sciences, Faculty of > Geography
Date: 1996
Date Type: Submission
Library of Congress Subject Heading: Population forecasting

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