# Effective Tracking of Building Energy Use: Improving the Commercial Buildings and Residential Energy Consumption Surveys(2012)

## Chapter: Appendix E: Composite Estimation

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Page 105
Suggested Citation:"Appendix E: Composite Estimation." National Research Council. 2012. Effective Tracking of Building Energy Use: Improving the Commercial Buildings and Residential Energy Consumption Surveys. Washington, DC: The National Academies Press. doi: 10.17226/13360.
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Appendix E

Composite Estimation

This appendix briefly presents the simplest composite estimator for a rotating panel design. There is an extensive literature on composite estimation to which the reader can turn for descriptions of more elaborate approaches.

This discussion assumes an annual data collection. Let denote a parameter to be estimated in year t, and let denote the “usual unbiased estimate” of based only on data from time t. Note that it is also possible to compute an estimate based only on the overlapping (or “matched”) sample, i.e., using only the sample that is being interviewed for the second time (or more). Since values exist from the previous year for the matched sample, it is possible to compute and so is an estimate of the change between year t and year t–1 based on the same sample units. By adding this estimate of change to last year’s estimate of a second estimate of , namely, is obtained. (In words, the estimate for this year is equal to the estimate for last year plus the estimated year-to-year change.) Since there are now two estimates of the same quantity , it is natural to combine them to obtain an improved estimate. Thus the so-called composite estimate of is a weighted average of two estimates (assuming α is between 0 and 1). Because is a better estimate than then in practice, the following composite estimate is used: .

The value of α is chosen to minimize the variance of .

Page 106
Suggested Citation:"Appendix E: Composite Estimation." National Research Council. 2012. Effective Tracking of Building Energy Use: Improving the Commercial Buildings and Residential Energy Consumption Surveys. Washington, DC: The National Academies Press. doi: 10.17226/13360.
×

Page 105
Suggested Citation:"Appendix E: Composite Estimation." National Research Council. 2012. Effective Tracking of Building Energy Use: Improving the Commercial Buildings and Residential Energy Consumption Surveys. Washington, DC: The National Academies Press. doi: 10.17226/13360.
×
Page 106
Suggested Citation:"Appendix E: Composite Estimation." National Research Council. 2012. Effective Tracking of Building Energy Use: Improving the Commercial Buildings and Residential Energy Consumption Surveys. Washington, DC: The National Academies Press. doi: 10.17226/13360.
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Next: Appendix F: Letter Report »
Effective Tracking of Building Energy Use: Improving the Commercial Buildings and Residential Energy Consumption Surveys Get This Book
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The United States is responsible for nearly one-fifth of the world's energy consumption. Population growth, and the associated growth in housing, commercial floor space, transportation, goods, and services is expected to cause a 0.7 percent annual increase in energy demand for the foreseeable future. The energy used by the commercial and residential sectors represents approximately 40 percent of the nation's total energy consumption, and the share of these two sectors is expected to increase in the future.

The Commercial Buildings Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS) are two major surveys conducted by the Energy Information Administration. The surveys are the most relevant sources of data available to researchers and policy makers on energy consumption in the commercial and residential sectors. Many of the design decisions and operational procedures for the CBECS and RECS were developed in the 1970s and 1980s, and resource limitations during much of the time since then have prevented EIA from making significant changes to the data collections. Effective Tracking of Building Energy Use makes recommendations for redesigning the surveys based on a review of evolving data user needs and an assessment of new developments in relevant survey methods.

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