A Benchmarking Approach to Temporal Disaggregation of Economic Time Series by Related Series

• Published in: Communications in statistics. Theory and methods 2009-01, Vol.38 (19), p.3561-3582
• Main Authors: Zaier, L. Hedhili, Trabelsi, A.
• Format: Article
• Language: English
• Subjects: Applications; ARIMA model-based approach (AMB); Benchmarking; Consistency; Exact sciences and technology; General topics; Inference from stochastic processes; time series analysis; Insurance, economics, finance; Mathematics; Probability and statistics; Sciences and techniques of general use; Signal extraction; Statistics; Temporal disaggregation; Unknown components ARIMA (UCARIMA) model
• ISSN: 0361-0926; 1532-415X
• DOI: 10.1080/03610920802604194

Several methods have been devised to deal with the problem of temporal disaggregation of economic time series (a) either when related series are available or (b) when only aggregate figures exist. In this article, we propose a statistical model-based approach to temporal disaggregation of economic time series by related series. The proposed approach is performed in two stages. In the first stage, we evaluate a preliminary estimate of the disaggregated series using a regression model for the disaggregated series and related series observed in the same frequency. The preliminary estimate of disaggregated series obtained in the first step is not consistent with aggregate figures. To ensure consistency we propose in the second stage, the use of a modified benchmarking approach based on signal extraction (Hillmer and Trabelsi, 1987 ; Trabelsi and Hillmer, 1990 ) to adjust the preliminary estimate of disaggregate series. The approach developed here is used for Seasonally Adjusted (SA) and Not Seasonally Adjusted (NSA) data. A comparison with previous temporal disaggregation methods has been done.