Evaluating two model reduction approaches for large scale hedonic models sensitive to omitted variables and multicollinearity

Toke Panduro () and Bo Thorsen

Letters in Spatial and Resource Sciences, 2014, vol. 7, issue 2, 85-102

Abstract: Hedonic models in environmental valuation studies have grown in terms of number of transactions and number of explanatory variables. We focus on the practical challenge of model reduction, when aiming for reliable parsimonious models, sensitive to omitted variable bias and multicollinearity. We evaluate two common model reduction approaches in an empirical case. The first relies on a principal component analysis (PCA) used to construct new orthogonal variables, which are applied in the hedonic model. The second relies on a stepwise model reduction based on the variance inflation index and Akaike’s information criteria. Our empirical application focuses on estimating the implicit price of forest proximity in a Danish case area, with a dataset containing 86 relevant variables. We demonstrate that the estimated implicit price for forest proximity, while positive in all models, is clearly sensitive to the choice of approach, as the PCA reduced model produces a parameter estimate double the size of the alternative models. While PCA is an attractive variable reduction approach, it may result in an important loss of information relative to the stepwise reduction information based approach. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Forest proximity; Spatial autocorrelation; GIS; Principal component analysis; Q51; R15; R21; R31 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1007/s12076-013-0103-x (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lsprsc:v:7:y:2014:i:2:p:85-102

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/12076

DOI: 10.1007/s12076-013-0103-x

Access Statistics for this article

Letters in Spatial and Resource Sciences is currently edited by Henk Folmer and Amitrajeet A. Batabyal

More articles in Letters in Spatial and Resource Sciences from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().