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Faster fiscal stimulus and a higher government spending multiplier in China: Mixed-frequency identification with SVAR

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Listed:
  • Mingyang Li
  • Linlin Niu
Abstract
Motivating with two scenarios in which the government spending in China timely reacted to output shock within a quarter, this letter points out a downward bias in the estimation of Chinese government spending multiplier using the classical lag restriction for shock identification in a quarterly SVAR framework à la Blanchard and Perotti (2002). By relaxing the lag-length restriction from one quarter to one month, we propose a mixed-frequency identification (MFI) strategy by taking the unexpected spending change in the first month of each quarter as an instrument. The estimation results show that the Chinese government significantly reacts to output shock counter-cyclically within a quarter, with the resulting government spending multiplier being 0.546 on impact and 1.849 at the maximum. A comparison study confirms that results based on the identification strategy of Blanchard and Perotti (2002) suffer severe downward bias in such a case.

Suggested Citation

  • Mingyang Li & Linlin Niu, 2021. "Faster fiscal stimulus and a higher government spending multiplier in China: Mixed-frequency identification with SVAR," Working Papers 2021-10-19, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    • Handle: RePEc:wyi:wpaper:002594
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    References listed on IDEAS

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    1. Jacopo Cimadomo & Antonello D'Agostino, 2016. "Combining Time Variation and Mixed Frequencies: an Analysis of Government Spending Multipliers in Italy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1276-1290, November.
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    5. Alan Auerbach & Yuriy Gorodnichenko & Daniel Murphy, 2020. "Local Fiscal Multipliers and Fiscal Spillovers in the USA," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 68(1), pages 195-229, March.
    6. Karel Mertens & Morten O. Ravn, 2013. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States," American Economic Review, American Economic Association, vol. 103(4), pages 1212-1247, June.
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    8. Haroon Mumtaz & Laura Sunder‐Plassmann, 2021. "Nonlinear effects of government spending shocks in the USA: Evidence from state‐level data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 86-97, January.
    9. Olivier Blanchard & Roberto Perotti, 2002. "An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1329-1368.
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    More about this item

    Keywords

    government spending multiplier; inside lag; mixed-frequency identification; SVAR model.;
    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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory

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