Spatial Econometrics in Practice

Authored by: Pedro Pires de Matos

The Routledge Handbook of Planning Research Methods

Print publication date:  November  2014
Online publication date:  August  2014

Print ISBN: 9780415727952
eBook ISBN: 9781315851884
Adobe ISBN: 9781317917038


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Space is a fundamental dimension of planning. Most of the subjects that planners work with are geographically bounded or exhibit some degree of interaction that has spatial ramifications, from the larger units such as countries and regions down to the micro-level of firms, households and individuals. In a similar fashion, these subjects are also temporally bounded, in that they are embedded in a timeline that defines past, present and future. Researchers working in the fields of economic growth, labour economics and finance, to name only a few, have long since incorporated the effects of serial correlation (or autocorrelation, i.e. observations being correlated with themselves over specific time intervals) in their theoretical and empirical models.1 They do so because it is logical to assume that past events and actions often influence current and future behaviour. Some of the mechanisms driving serial correlation include persistence, cyclical patterns and path dependency (i.e. current decisions being constrained by past decisions, or “history matters”). In short, many agents have memory, so that their past behaviour helps to understand their current actions and informs about their likely future decisions.

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