Some Proposed Optional Estimators for Totals and their Relative Standard Errors for a set of Weekly Cutoff Sample Establishment Surveys

by James R. Knaub, Jr.

Abstract: Establishment surveys collected weekly are obvious candidates for cutoff sampling with prediction for estimation of the universe and estimation of relative standard errors for estimated totals, if there are good regressor data available. In the case investigated here, there are monthly census data that may be used for regressor data. The same data element/attribute collected in a previous census is often the best single regressor, but there may be others. For example, crude oil imports by country of origin sampled weekly may use the same data element from a monthly sample as one regressor, but may also use imports from all other countries as a second regressor, as suppliers may change. This is analogous to fuel switching for electric power plants, Knaub (2003), where multiple regression estimation has been used. Small area estimation is used there, and may also be used here. A number of optional estimators for totals and their variances are suggested, and some of them are examined closely below for one regressor. This is preliminary work in an attempt to improve estimation, and to provide estimates of relative standard errors (RSEs) for better decision making by US Energy Information Administration (EIA) staff, regarding what data to collect, and for customers of EIA publications to make informed use of these results. Currently, forecasting is used for imputation for the weekly petroleum survey considered here, but prediction has been proven, see Knaub (1999, 2009), to perform well for this also, and includes the impact on the RSE estimates, and will cause results to better reflect the current status of the petroleum market. Methods developed for these surveys, could easily apply to other establishment surveys. The simplest option here is also the newest method being used for a survey of natural gas. This is part of ongoing work at the US EIA.

Key Words: Quasi-cutoff sampling, small area estimation, borrowing strength, prediction, weighted least squares regression, combined estimator

James R. Knaub, Jr.,

Editor:Richard G. Graf,

NOTE: This article was revised on March 1, 2014.

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