Estimation of gravity models by OLS estimation, NLS estimation, Poisson, and Neural Network specifications
1997 (English)Report (Other academic)
Four specifications of gravity models for freight flow prediction are compared. The traditional specification with OLS estimation is compared with non-linear least squares (NLS) estimation as well as with a model where data are assumed to be Poisson distributed. These are compared with a semi-parametric neural network model. Data consists of freight flows between Norwegian counties. The attribute describing a node is population while distance gives the friction on links of transportation. Results show that estimation with OLS and NLS is inferior to Poisson and Neural Network specifications. However, the Poisson model, although advantageous compared to OLS, may still be improved upon. The semiparametric Neural Network does require less of these restrictions to hold and also outperforms the others as a tool for forecast in terms of Root Mean Square Error (RMSE). The NLS model although showed the best performance when estimated on known data.
Place, publisher, year, edition, pages
1997. , 16 p.
, Regional Dimensions Working Paper, ISSN 1400-4526 ; 6
Gravity model, Transportation, Freight flows, Spatial interaction, OLS, Poisson-regression, Non-linear regression, Neural network
IdentifiersURN: urn:nbn:se:umu:diva-24755OAI: oai:DiVA.org:umu-24755DiVA: diva2:227406
Distributor:Centrum för regionalvetenskap (CERUM), 90187, Umeå
Financial support has been received from the Swedish Transport and Communications Research Board (KFB)2009-07-132009-07-132009-07-13Bibliographically approved