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Disaggregation of annual time‐series data to quarterly figures: A comparative study
Author(s) -
Chan WaiSum
Publication year - 1993
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980120816
Subject(s) - series (stratigraphy) , computer science , monte carlo method , data set , set (abstract data type) , sample (material) , econometrics , time series , statistics , machine learning , mathematics , artificial intelligence , paleontology , chemistry , chromatography , biology , programming language
In this paper we consider some of the prominent methods that are available in the literature for the problem of disaggregating annual time‐series data to quarterly figures. The procedures are briefly described and illustrated through a real data set. The performances of the methods are compared in a Monte Carlo study. The results indicate that the complicated model‐based procedure is usually superior to other non‐model‐based alternatives in the large sample situations. Based on the simulation results, we make some recommendations regarding the use of these methods.