Disaggregation of very small time series with multiple endogenous partial structural breaks

Jérôme Trinh ()
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Jérôme Trinh: Université de Cergy-Pontoise, THEMA

No 2022-10, THEMA Working Papers from THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise

Abstract: In this paper, we propose a method to disaggregate very small time series by fitting them with higher frequency related series using a cointegration regression with multiple partial endogenous structural breaks. We allow any coecient to change at up to two dates of structural break and three related series and provide critical values for the test of cointegration corrected for the very small sample size. We find that increasing the num- ber of related series drastically improves the power of the test by allowing for increased flexibility in the cointegration model. The simulated power of the test is shown to be very high even in very small sample sizes such as fifteen observations. This flexibility also mildly improves the accuracy of the disaggregation method when the sample size is as small as thirty-five observations. An application to the Chinese national accounts data is provided and allows the study of the Chinese business cycles stylized facts. We find that household consumption, public spending, and trade surpluses are the main driver of the business cycle.

Keywords: Time series; macroeconomic forecasting; disaggregation; structural change; business cycles; emerging economies (search for similar items in EconPapers)
JEL-codes: C32 E17 E37 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-cwa, nep-ecm, nep-ets and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:ema:worpap:2022-10

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