Model Uncertainty in Matrix Exponential Spatial Growth Regression Models. Issue 3 (17th September 2014)
- Record Type:
- Journal Article
- Title:
- Model Uncertainty in Matrix Exponential Spatial Growth Regression Models. Issue 3 (17th September 2014)
- Main Title:
- Model Uncertainty in Matrix Exponential Spatial Growth Regression Models
- Authors:
- Piribauer, Philipp
Fischer, Manfred M. - Abstract:
- <abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>This article considers the most important aspects of model uncertainty for spatial regression models, namely, the appropriate spatial weight matrix to be employed and the appropriate explanatory variables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models used in the regional growth literature, and develop a simple Bayesian model‐averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on previous work by reducing the computational costs through the use of Bayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial Durbin matrix exponential model has theoretical and computational advantages over the spatial autoregressive specification due to the ease of inversion, differentiation, and integration of the matrix exponential. In particular, the matrix exponential has a simple matrix determinant that vanishes for the case of a spatial weight matrix with a trace of zero. This allows for a larger domain of spatial growth regression models to be analyzed with this approach, including models based on different classes of spatial weight matrices. The working of the approach is illustrated for the case of 32 potential determinants and three classes of spatial weight matrices (contiguity‐based,<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>This article considers the most important aspects of model uncertainty for spatial regression models, namely, the appropriate spatial weight matrix to be employed and the appropriate explanatory variables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models used in the regional growth literature, and develop a simple Bayesian model‐averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on previous work by reducing the computational costs through the use of Bayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial Durbin matrix exponential model has theoretical and computational advantages over the spatial autoregressive specification due to the ease of inversion, differentiation, and integration of the matrix exponential. In particular, the matrix exponential has a simple matrix determinant that vanishes for the case of a spatial weight matrix with a trace of zero. This allows for a larger domain of spatial growth regression models to be analyzed with this approach, including models based on different classes of spatial weight matrices. The working of the approach is illustrated for the case of 32 potential determinants and three classes of spatial weight matrices (contiguity‐based, <italic>k</italic>‐nearest neighbor, and distance‐based spatial weight matrices), using a data set of income per capita growth for 273 European regions.</p> </abstract> … (more)
- Is Part Of:
- Geographical analysis. Volume 47:Issue 3(2015)
- Journal:
- Geographical analysis
- Issue:
- Volume 47:Issue 3(2015)
- Issue Display:
- Volume 47, Issue 3 (2015)
- Year:
- 2015
- Volume:
- 47
- Issue:
- 3
- Issue Sort Value:
- 2015-0047-0003-0000
- Page Start:
- 240
- Page End:
- 261
- Publication Date:
- 2014-09-17
- Subjects:
- Geography -- Methodology -- Periodicals
Electronic journals
910.00182 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1538-4632 ↗
http://onlinelibrary.wiley.com/ ↗
http://firstsearch.oclc.org ↗
http://firstsearch.oclc.org/journal=0016-7363;screen=info;ECOIP ↗ - DOI:
- 10.1111/gean.12057 ↗
- Languages:
- English
- ISSNs:
- 0016-7363
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4125.440000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 4370.xml