Temporal disaggregation of short time series with structural breaks: Estimating quarterly data from yearly emerging economies data

Jérôme Trinh ()
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Jérôme Trinh: Institut Polytechnique de Paris, CREST; Thema, University of Cergy-Pontoise.

No 2019-11, Working Papers from Center for Research in Economics and Statistics

Abstract: his article develops a methodology to compute up-to-date quarterly macroeconomic data for emerging countries by adapting a well known method of temporal disaggregation to time series with small sample size and instable relationships between them. By incorporating di erent procedures of structural break detection, the prediction of higher-frequency estimations of yearly oficial data can be improved. A methodology with a model selection procedure and disaggregation formulas is proposed. Its predictive performance is assessed by using empirical advanced countries data and simulated time series. An application to the Chinese national accounts allows the estimation of the cyclical components of the Chinese expenditure accounts and shows the Chinese economy to have second order moments more in line with emerging countries than advanced economies like the United States.

Keywords: Time series; macroeconomic forecasting; disaggregation; structural change; business cycles; emerging economies (search for similar items in EconPapers)
Pages: 41 pages
Date: 2019-06-27
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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