JSM 2005 Online Program

Abstract #303544

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	397
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Government Statistics
Abstract - #303544
Title:	Data Modeling for a Simulation Study of the Quarterly Financial Report Estimator
Author(s):	Donald M. Luery*+
Companies:	U.S. Census Bureau
Address:	1009 Turney Ave, Laurel, MD, 20707,
Keywords:	Time series ; Mixed Model ; Markov chain ; Student-t distributed noise
Abstract:	U.S. Census Bureau staff conducted a simulation study into alternative estimators for the Quarterly Financial Report (QFR). The QFR collects income statement and balance sheet data quarterly from samples of manufacturing, mining, wholesale trade, and retail trade companies. QFR data was first published in 1947 by the Federal Trade Commission and was transferred to the U.S. Census Bureau in 1982. The QFR uses a poststratified estimator that does not directly use the sample weights. The poststrata totals are unknown, so they are estimated by projecting forward sample estimates (weighted) from previous years. Several research projects have been conducted looking into aspects of this estimator, but none have been a comprehensive investigation looking at all aspects of the sample design and estimation. In 2003 and 2004, the Census Bureau conducted such an investigation through a simulation study of an artificial population that mimics the QFR sample design and data. This involved generating times series data for each of the companies in the artificial population. This paper discusses modeling the QFR data and generating the time series.

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