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. | |