Applying models for ordinal logistic regression to the analysis of household electricity consumption classes in Rio de Janeiro, Brazil
This study applies the proportional odds and partial proportional odds models for ordinal logistic regression to analyze household electricity consumption classes. Micro-data from households situated in the state of Rio de Janeiro during 2004 was used to measure the performance of the models in correctly classifying household electricity consumption classes via sociodemographic, electricity usage and dwelling characteristics. The strategy of using binary logistic regressions to test the main hypothesis of the proportional odds model, suggested by Bender and Grouven, was successful in identifying which of the independent variables could be estimated via the proportional odds assumption. Results indicate that the partial proportional odds models is slightly superior to the more restrictive approach. The study includes probabilistic examples to describe how changes in the independent variables affect the probability of a household belonging to specific classes of electricity consumption. Projections using the final model indicated that the approach may be useful for estimating aggregate household electricity consumption.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jung, Tae Yong, 1993. "Ordered logit model for residential electricity demand in Korea," Energy Economics, Elsevier, vol. 15(3), pages 205-209, July.
- Vincent Kang Fu, 1999. "Estimating generalized ordered logit models," Stata Technical Bulletin, StataCorp LP, vol. 8(44).
- Richard Williams, 2006. "Generalized ordered logit/partial proportional odds models for ordinal dependent variables," Stata Journal, StataCorp LP, vol. 6(1), pages 58-82, March.
- Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 14(1), pages 1-73, June.
- Tiwari, Piyush, 2000. "Architectural, Demographic, and Economic Causes of Electricity Consumption in Bombay," Journal of Policy Modeling, Elsevier, vol. 22(1), pages 81-98, January.
- Kahn, E. & Sathaye, J. & Robbins, D., 1986. "An engineering-economic approach to estimating the price elasticity of residential electricity demand," Energy Economics, Elsevier, vol. 8(2), pages 118-126, April.
When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:30:y:2008:i:4:p:1672-1692. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.