Infante, Enrico (2017) Two-Step Reconciliation of Time Series New Formulation and Validation. [Tesi di dottorato]


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Item Type: Tesi di dottorato
Lingua: English
Title: Two-Step Reconciliation of Time Series New Formulation and Validation
Date: 10 April 2017
Number of Pages: 210
Institution: Università degli Studi di Napoli Federico II
Department: Scienze Economiche e Statistiche
Dottorato: Statistica
Ciclo di dottorato: 28
Coordinatore del Corso di dottorato:
Lauro, Carlo
Date: 10 April 2017
Number of Pages: 210
Uncontrolled Keywords: Benchmarking, Balancing, Reconciliation
Settori scientifico-disciplinari del MIUR: Area 13 - Scienze economiche e statistiche > SECS-S/01 - Statistica
Area 13 - Scienze economiche e statistiche > SECS-S/03 - Statistica economica
Date Deposited: 04 May 2017 12:32
Last Modified: 14 Mar 2018 14:08
DOI: 10.6093/UNINA/FEDOA/11731


Two-step reconciliation methods solve the temporal constraint in the first step, while in the second step the contemporaneous constraint is satisfied without altering the temporal constraint. Both in Quenneville and Rancourt and in Di Fonzo and Marini methods, the methodology used applies the Denton benchmarking technique in the first step. The work done in this study is based on an alternative two-step procedure for the reconciliation of systems of time series, proposing an algorithm which allows to choose one of the two different solutions for the second step, and introduces the possibility of using well-known established techniques in the first step, such a Chow and Lin, Fernandez and Litterman. Furthermore, a way of dealing with the reconciliation of hierarchical systems of time series is presented. An innovative test for detecting common seasonal patterns in time series is also presented. Such test could be used for deciding at which level to seasonally adjust an aggregated time series before applying reconciliation. Moreover, together with a simulation study, several aspects of the validation of a reconciliation technique are shown, including a new methodology for detecting whether the outliers at the end of series are consistent. Two real examples using the European industrial production index and the euro area quarterly sector accounts data will also be presented.


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