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Disaggregating the Chinese annual national accounts to quarterly series

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  • Jérôme TRINH

    (Université de Cergy-Pontoise, THEMA)

Abstract

This article develops a methodology to compute up-to-date quarterly macroeco- nomic data for emerging countries by adapting a well-known method of temporal disaggregation to time series with very small sample size and unstable relationships between them. By incorporating di erent procedures of structural break detection, the prediction of higher-frequency estimations of yearly ocial data can be improved. A methodology with a model selection procedure and disaggregation formulas is pro- posed. Its predictive performance is assessed by using empirical advanced countries data and simulated time series. An application to the Chinese national accounts al- lows 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.

Suggested Citation

  • Jérôme TRINH, 2019. "Disaggregating the Chinese annual national accounts to quarterly series," THEMA Working Papers 2019-08, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  • Handle: RePEc:ema:worpap:2019-08
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    References listed on IDEAS

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    More about this item

    Keywords

    Time series; macroeconomic forecasting; disaggregation; structural change; business cycles; emerging economies;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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