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
页数:38
相关论文
共 50 条
  • [1] A Discussion of Non-Gaussian Price Processes for Energy and Commodity Operations
    Gambaro, Anna Maria
    Secomandi, Nicola
    PRODUCTION AND OPERATIONS MANAGEMENT, 2021, 30 (01) : 47 - 67
  • [2] Locally robust inference for non-Gaussian SVAR models
    Hoesch, Lukas
    Lee, Adam
    Mesters, Geert
    QUANTITATIVE ECONOMICS, 2024, 15 (02) : 523 - 570
  • [3] RAIM with Non-Gaussian Errors
    Misra, Pratap
    Rife, Jason
    PROCEEDINGS OF THE 26TH INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS 2013), 2013, : 2664 - 2671
  • [4] Robust Degradation Analysis With Non-Gaussian Measurement Errors
    Zhai, Qingqing
    Ye, Zhi-Sheng
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2017, 66 (11) : 2803 - 2812
  • [5] Correlating Twitter with the Stock Market Through Non-Gaussian SVAR
    Zhao, Shuai
    Tong, Yunhai
    Liu, Xinhai
    Tan, Shaohua
    2016 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI), 2016, : 257 - 264
  • [6] Non-Gaussian Berkson errors in bioassay
    Althubaiti, Alaa
    Donev, Alexander
    STATISTICAL METHODS IN MEDICAL RESEARCH, 2016, 25 (01) : 430 - 445
  • [7] Monetary versus fiscal policy in India: an SVAR analysis
    Arora, Sanchit
    MACROECONOMICS AND FINANCE IN EMERGING MARKET ECONOMIES, 2018, 11 (03) : 250 - 274
  • [8] Learning and correcting non-Gaussian model errors
    Smyl, Danny
    Tallman, Tyler N.
    Black, Jonathan A.
    Hauptmann, Andreas
    Liu, Dong
    JOURNAL OF COMPUTATIONAL PHYSICS, 2021, 432 (432)
  • [9] Non-Gaussian errors of baryonic acoustic oscillations
    Ngan, W.
    Harnois-Deraps, J.
    Pen, U. -L.
    McDonald, P.
    MacDonald, I.
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2012, 419 (04) : 2949 - 2960
  • [10] APERTURE ANTENNA WITH NON-GAUSSIAN PHASE ERRORS
    BECKMANN, P
    PROCEEDINGS OF THE IEEE, 1974, 62 (04) : 532 - 533