Interpolating and Standardizing Time Series Data Covering Various Fiscal Intervals Using Splines

• Activity Number: 83
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
• Sponsor: Survey Research Methods Section
• Author(s): Jack Lothian*+
• Companies: Statistics New Zealand
• Keywords: Calendarization ; Administrative data ; Significance editing ; Cubic splines ; Standardization ; Imputation

Abstract

Statistics New Zealand is in the midst of implementing its 2010-2020 Strategic Plan that will transform how the agency functions. The "administrative data first" philosophy is a critical component in the transformation process and the Goods and Sales Tax (GST) data is a key dataset for transforming business surveys. GST can provide data which can be used in sub-annual surveys to replace directly surveyed units or to improve in editing or in calibration. These processes could potentially reduce response burden and collection costs plus improve quality. However, the use of administrative data poses major challenges. Due to late filings, GST data are not all available on time during the production cycle and the data covers a melange of varying and overlapping time intervals. We propose a calendarization method based on interpolating the cumulated flows with splines that provides data with standardization time intervals and short-term forecasts. The methodology improves the timeliness and quality of the GST data and increases the willingness of the survey programs to embrace tax data.