Richard Laferrière

Spatial autoregressive error components in travel flow models

Abstract In this study, we propose a generalization of the error components formulation to model the correlation among the errors of a regression based on travel flow data. The error term is broken down into a sum of one component related to the origin zones, one component related to the destination zones and a remainder. The inter-dependences among the errors are assumed to result from applying a first-order spatial autoregressive generating process to each component. An efficient estimation approach based on maximum likelihood is suggested to address the practical implementation of such a model with a large sample size.

Spatial Autoregressive Error Components in Travel Flow Models: An Application to Aggregate Mode Choice

In this chapter we use empirical examples to demonstrate the usefulness of the generalized error component framework suggested in Bolduc et al. (1992) for dealing with the problem of correlation among the errors of a regression based on travel flow data. This methodology augments Standard error component decompositions with first-order spatial autoregressive processes, i.e., SAR(l), with the purpose of allowing for the different sources of misspecification generally associated with this type of model. The error component approach splits the error term into a sum of one component related to the zones in origin, one component associated with the zones in destination and a remainder. The interdependencies among the errors are modeled with the help of SAR(l) processes. This decompositional approach extends the previous works by Brandsma and Ketellapper (1979) and Bolduc et al. (1989) which also relied on spatial autoregressive processes to model the error correlation.

The box-cox transformation : Power invariance and a new interpretation

Abstract We show that a Box-Cox transformation on the dependent variable of linear regression models is invariant to power transformations of that variable even without the presence of a regression constant and, consequently, can sometimes be interpreted as a simple power transformation the estimation of which will then admit of a non-degenerate solution.