A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Locational error in the estimation of regional discrete choice models using distance as a regressor
https://orcid.org/0000-0003-0984-4288
Abstract In many microeconometric studies distance from a relevant point of interest (such as a hospital) is often used as a predictor in a regression framework. Confidentiality rules, often, require to geo-mask spatial micro-data, reducing the quality of such relevant information and distorting inference on models’ parameters. This paper extends previous literature, extending the classical results on the measurement error in a linear regression model to the case of hospital choice, showing that in a discrete choice model the higher is the distortion produced by the geo-masking, the higher will be the downward bias in absolute value toward zero of the coefficient associated to the distance in the models. Monte Carlo simulations allow us to provide evidence of theoretical hypothesis. Results can be used by the data producers to choose the optimal value of the parameters of geo-masking preserving confidentiality, not destroying the statistical information.
Locational error in the estimation of regional discrete choice models using distance as a regressor
https://orcid.org/0000-0003-0984-4288
Abstract In many microeconometric studies distance from a relevant point of interest (such as a hospital) is often used as a predictor in a regression framework. Confidentiality rules, often, require to geo-mask spatial micro-data, reducing the quality of such relevant information and distorting inference on models’ parameters. This paper extends previous literature, extending the classical results on the measurement error in a linear regression model to the case of hospital choice, showing that in a discrete choice model the higher is the distortion produced by the geo-masking, the higher will be the downward bias in absolute value toward zero of the coefficient associated to the distance in the models. Monte Carlo simulations allow us to provide evidence of theoretical hypothesis. Results can be used by the data producers to choose the optimal value of the parameters of geo-masking preserving confidentiality, not destroying the statistical information.
Locational error in the estimation of regional discrete choice models using distance as a regressor
https://orcid.org/0000-0003-0984-4288
Abstract In many microeconometric studies distance from a relevant point of interest (such as a hospital) is often used as a predictor in a regression framework. Confidentiality rules, often, require to geo-mask spatial micro-data, reducing the quality of such relevant information and distorting inference on models’ parameters. This paper extends previous literature, extending the classical results on the measurement error in a linear regression model to the case of hospital choice, showing that in a discrete choice model the higher is the distortion produced by the geo-masking, the higher will be the downward bias in absolute value toward zero of the coefficient associated to the distance in the models. Monte Carlo simulations allow us to provide evidence of theoretical hypothesis. Results can be used by the data producers to choose the optimal value of the parameters of geo-masking preserving confidentiality, not destroying the statistical information.
Locational error in the estimation of regional discrete choice models using distance as a regressor
Arbia, Giuseppe (author) / Berta, Paolo (author) / Dolan, Carrie B. (author)
2022
Article (Journal)
Electronic Resource
English
BKL:
83.64$jRegionalwirtschaft
/
74.12
Stadtgeographie, Siedlungsgeographie
/
38.00$jGeowissenschaften: Allgemeines
/
38.00
Geowissenschaften: Allgemeines
/
83.64
Regionalwirtschaft
/
74.12$jStadtgeographie$jSiedlungsgeographie
RVK:
ELIB39
/
ELIB18
/
ELIB45
Local classification FBW:
oek 4450
Estimation of Regressor Reliability
| British Library Online Contents | 2008
Locational choice and the individual firm
| Taylor & Francis Verlag | 1969
Business networks and suppliers' locational choice
| Online Contents | 2002
Optimal transportation policy with strategic locational choice
| Online Contents | 1995
Endogenous R&D spillovers and locational choice
| Online Contents | 2005