Table of Contents

Handbook of Research Methods and Applications in Economic Geography

Handbook of Research Methods and Applications in Economic Geography

Handbooks of Research Methods and Applications series

Edited by Charlie Karlsson, Martin Andersson and Therese Norman

The main purpose of this Handbook is to provide overviews and assessments of the state-of-the-art regarding research methods, approaches and applications central to economic geography. The chapters are written by distinguished researchers from a variety of scholarly traditions and with a background in different academic disciplines including economics, economic, human and cultural geography, and economic history. The resulting handbook covers a broad spectrum of methodologies and approaches applicable in analyses pertaining to the geography of economic activities and economic outcomes.

Chapter 7: Simultaneous-equations analysis in regional science and economic geography

Timo Mitze and Andreas Stephan

Subjects: economics and finance, regional economics, geography, economic geography, research methods in geography, research methods, research methods in economics, research methods in geography, urban and regional studies, regional economics, research methods in urban and regional studies


The specification and estimation of simultaneous-equations models (henceforth SEMs) has a long tradition in economics. Although SEMs were originally established in the field of macroeconomics, various applications can also be found in applied regional science and economic geography. Among many others, one prominent example is the Carlino and Mills (1987) study on the simultaneous evolution of regional population and employment densities, which gave rise to the famous ‘chicken-or-egg’ quest for causality within the framework of regional adjustment models. And indeed, specifying and estimating an SEM has much to do with getting causal relationships right. This is why applied economists and econometricians generally valorize the SEM approach for its capacity to formulate an explicit structural model with more than just one endogenous variable and the statistical power to control for correlated residuals among the individual equations of the system. While the first argument is of crucial importance for the consistency of the estimated model parameters, the second point is mainly concerned with the notion of estimation efficiency.

You are not authenticated to view the full text of this chapter or article.

Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.

Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.

Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.

Further information