Benchmarking Temporal Distribution And Reconciliation Methods For Time Series
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Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series
Author: Estela Bee Dagum
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-09-23
Time series play a crucial role in modern economies at all levels of activity and are used by decision makers to plan for a better future. Before publication time series are subject to statistical adjustments and this is the first statistical book to systematically deal with the methods most often applied for such adjustments. Regression-based models are emphasized because of their clarity, ease of application, and superior results. Each topic is illustrated with real case examples. In order to facilitate understanding of their properties and limitations of the methods discussed a real data example is followed throughout the book.
On the Extrapolation with the Denton Proportional Benchmarking Method
Author: Mr.Tommaso Di Fonzo
language: en
Publisher: International Monetary Fund
Release Date: 2012-06-01
Statistical offices have often recourse to benchmarking methods for compiling quarterly national accounts (QNA). Benchmarking methods employ quarterly indicator series (i) to distribute annual, more reliable series of national accounts and (ii) to extrapolate the most recent quarters not yet covered by annual benchmarks. The Proportional First Differences (PFD) benchmarking method proposed by Denton (1971) is a widely used solution for distribution, but in extrapolation it may suffer when the movements in the indicator series do not match consistently the movements in the target annual benchmarks. For this reason, an enhanced formula for extrapolation was recommended by the IMF’s Quarterly National Accounts Manual: Concepts, Data Sources, and Compilation (2001). We discuss the rationale behind this technique, and propose a matrix formulation of it. In addition, we present applications of the enhanced formula to artificial and real-life benchmarking examples showing how the extrapolations for the most recent quarters can be improved.
Complex Models and Computational Methods in Statistics
Author: Matteo Grigoletto
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-01-26
The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.