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Missing Survey Data in End-Use Energy Models: An Overlooked Problem

Although missing data are found in all types of data sets, surveys are particularly prone to produce data sets in which values of some respondent variables are missing (see, e.g., Cochran, 1977; Ericson, 1967; Kalton, 1983; and Hutcheson and Prather, 1977). Survey data collected for end-use energy demand models are no exception; high frequencies of nonresponse occur for many variables. This issue is, however, generally disregarded in the end-use literature, and analysts working with end-use models often discard cases in which values are missing for variables required by their models (see e.g., U.S. Government, 1983; Pacific Gas and Electric, 1983; Hirst and Carney, 1978; and EPRI, 1977). Discarding cases with missing values has important consequences. It implicitly assumes that the missing values occur randomly rather than systematically. If, however, missing values do not occur randomly, discarding cases with missing values will result in misspecified models and biased forecasts. Furthermore, by discarding cases, the detail appropriate for a given end-use model can be lost.

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Energy Specializations: Energy Modeling – Sectoral Energy Demand & Technology; Energy Modeling – Other

JEL Codes:
Q55 - Environmental Economics: Technological Innovation
C59 - Econometric Modeling: Other

Keywords: End use energy demand models, Missing data, Misspecified models, biased forecasts

DOI: 10.5547/ISSN0195-6574-EJ-Vol7-No3-12

Published in Volume 7, Number 3 of The Quarterly Journal of the IAEE's Energy Economics Education Foundation.