Commodity Price and Indonesian Fiscal Policy: An SVAR Analysis with Non-Gaussian Errors

被引:0
作者
Mansur, Alfan [1 ,2 ]
机构
[1] Univ Helsinki, Helsinki 00014, Finland
[2] Minist Finance Republ Indonesia, Jakarta 10710, Indonesia
关键词
fiscal; income tax; spending; commodity; non-Gaussian; GOVERNMENT; INCOME; IDENTIFICATION; TAXATION;
D O I
10.1515/jtse-2023-0037
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
This study exploits the non-Gaussianity for identification of a Bayesian SVAR model on newly unexplored monthly Indonesian data from 2007M1-2022M12, where we disentangle the commodity-related revenue from the total government revenues. Our main contribution is in labeling the statistically identified structural shocks as economic shocks by conducting a formal assessment of a set of proposed sign constraints. We simultaneously label a commodity price and three fiscal policy shocks, i.e. fiscal income tax, investment-spending, and consumption-spending shocks. Having evaluated their impacts, among the fiscal policy shocks, we find income tax shock the most impactful on output. Moreover, during the Covid crisis 2020-2021, the launched fiscal economic stimulus package (PEN program) positively contributed to the output. The recession of the Covid crisis could have deepened had the fiscal policymaker not responded at all. Albeit so, we should not overlook the contribution of the rising commodity prices to the output recovery. We also evaluate the commodity boom period in 2007-2009, the tax amnesty program in 2016-2017 and 2022, and the infrastructure spending boost in 2015. Our results suggest that output and retail sales would have been lower without the commodity price shock's contribution during the commodity boom. Then, we find that tax amnesty and infrastructure spending boost policies contribute to higher retail sales.
引用
收藏
页码:29 / 66
页数:38
相关论文
共 34 条
  • [1] Locally robust inference for non-Gaussian SVAR models
    Hoesch, Lukas
    Lee, Adam
    Mesters, Geert
    QUANTITATIVE ECONOMICS, 2024, 15 (02) : 523 - 570
  • [2] Non-Gaussian analysis of noise for metal interconnection electromigration
    He Liang
    Du Lei
    Huang Xiao-Jun
    Chen Hua
    Chen Wen-Hao
    Sun Peng
    Han Liang
    ACTA PHYSICA SINICA, 2012, 61 (20)
  • [3] Non-Gaussian Penalized PARAFAC Analysis for fMRI Data
    Liang, Jingsai
    Zou, Jiancheng
    Hong, Don
    FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS, 2019, 5
  • [4] Compressed Principal Component Analysis of Non-Gaussian Vectors
    Mignolet, Marc
    Soize, Christian
    SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 2020, 8 (04): : 1261 - 1286
  • [5] Do we reject restrictions identifying fiscal shocks? identification based on non-Gaussian innovations
    Karamysheva, Madina
    Skrobotov, Anton
    JOURNAL OF ECONOMIC DYNAMICS & CONTROL, 2022, 138
  • [6] Artificial neural network for Gaussian and non-Gaussian random fatigue loading analysis
    Durodola, J. F.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2019, 233 (23-24) : 7525 - 7544
  • [7] MRI quantification of non-Gaussian water diffusion by kurtosis analysis
    Jensen, Jens H.
    Helpern, Joseph A.
    NMR IN BIOMEDICINE, 2010, 23 (07) : 698 - 710
  • [8] Damage analysis of products under logistics non-Gaussian vibration
    Xie J.-L.
    Wang Z.-W.
    Zhendong Gongcheng Xuebao/Journal of Vibration Engineering, 2024, 37 (04): : 645 - 656
  • [9] Contact Analysis and Friction Prediction of Non-Gaussian Random Surfaces
    Ren, Jinzhao
    Yuan, Huiqun
    APPLIED SCIENCES-BASEL, 2022, 12 (21):
  • [10] Reliability analysis of wind turbines under non-Gaussian wind load
    Shuang, Miao
    Song, Bo
    STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, 2018, 27 (03)