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Measuring Fiscal Policy Uncertainty—Based on an Analysis Framework of Expectation with Adaptive Learning

【Authors】
GONG Min, ZHANG Fan & GAN Jiawu
【WorkUnit】
GONG Min (Hunan University of Technology and Business, 410205)ZHANG Fan (Zhejiang University of Finance and Economics, 310018)GAN Jiawu (Yunnan University of Finance and Economics, 650221)
【Abstract】

The fiscal policy in China attaches importance to addressing short-term problems but pays little attention to the stability in the long term, and little research has been conducted on fiscal policy uncertainty, in which measuring the uncertainty is a key and difficult point. Within the framework of adaptive learning expectation, this paper studies diffident learning models and finds that market entities in China tend to use the continuous learning strategy that emphasizes recent experiences to form the fiscal policy expectation which is not convergent to the rational expectation, and thus proves that the fiscal policy uncertainty exists in China. On top of that, the fiscal policy uncertainty is measured, and the result shows that the fiscal policy uncertainty in China is endogenous and systematic because the rule of fiscal policy is unstable dynamically and thus the expectancy model is also not stable. This paper finds that the fiscal policy uncertainty leads to a negative shock to production, price and export, and it produces the overshoot to production, which proves the consistence of the uncertainty index in this paper to a certain degree. At the end, this paper offers policy suggestions on the governance of fiscal policy uncertainty.


JEL:D83, E37, H39

【KeyWords】
Degree of Fiscal Policy Uncertainty, Learning Expectation Model, CG-LS Learning Strategy