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Activity Number: 333
Type: Invited
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Government Statistics Section
Abstract #318042
Title: Progressive Data Modeling
Author(s): Zahoor Ahmad* and Li-Chun Zhang
Companies: University of Southampton and University of Southampton
Keywords: progressive data ; existent population ; reporting population ; reporting probability ; informative reporting ; estimating equations
Abstract:

Currently a substantial initiative is taken at the National Statistical Offices to make full use of administrative data in statistical production. For example, several studies have previously been carried out at the United Kingdom Office of National Statistics (ONS), such as forecasting value-added-tax (VAT) turnover at the unit-level, adjusting VAT register totals towards the existing Monthly Business Survey (MBS) -based turnover estimates etc. The VAT data are said to be progressive in the sense that VAT reports (or observations) of a particular time period t of interest may arrive at various points long after t. For timely prediction of the VAT turnover total before all the data have arrived, a critical issue is when the timeliness of VAT reporting is related to VAT turnover i.e. informative reporting. In this work we develop new approaches for handling informative reporting, drawing on the relevant techniques for informative sampling and informative nonresponse. We study approaches to modelling the potential informative report, methods for estimation, including maximum likelihood and estimation equations, and illustrate the methodology.


Authors who are presenting talks have a * after their name.

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