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Investigation of the time-dependent dynamics between government revenue and expenditure in China: a wavelet approach

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  • Hyunjoo Kim Karlsson

Abstract

Unlike previous studies on causal relationships between government revenue and expenditures in China, this study takes into consideration structural breaks in the data by performing wavelet decomposition prior to testing for Granger causality between the fiscal components. The use of wavelet decomposition is motivated by economic theories, which suggest allowing for different budgetary considerations at different time horizons, as well as by the existence of special properties in the data in the form of unit roots and structural breaks. The results from the Granger causality test when using the wavelet-decomposed quarterly data over the period 1980–2015 indicate that government revenue Granger-causes government expenditure (tax-and-spend hypothesis) in the wavelet scales of two to four quarters. The results also show that bidirectional causality (fiscal synchronisation) exists in the wavelet scale of eight to sixteen quarters. Understanding the causal relationships between revenue and expenditure at different time scales is important for formulating relevant policy measures in order to maintain fiscal sustainability in China.

Suggested Citation

  • Hyunjoo Kim Karlsson, 2020. "Investigation of the time-dependent dynamics between government revenue and expenditure in China: a wavelet approach," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 25(2), pages 250-269, April.
    • Handle: RePEc:taf:rjapxx:v:25:y:2020:i:2:p:250-269
    DOI: 10.1080/13547860.2019.1646573
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    Cited by:

    1. Solikin, Akhmad & Nizar, Muhammad Afdi, 2022. "Government Revenue and Government Spending Nexus: A Testing Hypothesis for Indonesia," MPRA Paper 118556, University Library of Munich, Germany.
    2. Dervis Kirikkaleli & Bugra Ozbeser, 2023. "Government Expenditures and Tax Revenues in the United States of America," SAGE Open, , vol. 13(1), pages 21582440231, March.

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