Faster fiscal stimulus and a higher government spending multiplier in China: Mixed-frequency identification with SVAR
Mingyang Li and
Linlin Niu
Economics Letters, 2021, vol. 209, issue C
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.
Keywords: Government spending multiplier; Inside lag; Mixed-frequency identification; SVAR model (search for similar items in EconPapers)
JEL-codes: C32 C36 E23 E62 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176521004122
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Faster fiscal stimulus and a higher government spending multiplier in China: Mixed-frequency identification with SVAR (2021) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:209:y:2021:i:c:s0165176521004122
DOI: 10.1016/j.econlet.2021.110135
Access Statistics for this article
Economics Letters is currently edited by Economics Letters Editorial Office
More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().