Analysis of GDP in Countries allied to Indonesia using a Combination of the GSTAR Model and Verification using Statistical Quality Control
DOI:
https://doi.org/10.12962/ijcsam.v11i1.4307Kata Kunci:
Terms—spatial,, connected,, in-controlAbstrak
The Generalized Space-Time Autoregressive (GSTAR) model is used to model GDP growth rates in Indonesia, Malaysia, Singapore, and Brunei Darussalam, allied countries. Southeast Asian countries have cultural and historical linkages and often share economic tendencies. GSTAR is used because it can represent GDP dynamics' complex spatial and temporal relationships. Historical GDP data for the four countries from 1975 to the present is collected. The GSTAR model models regional interdependence and temporal patterns in these economies' geographical and temporal linkages. To test GSTAR model accuracy and robustness, control chart analysis is done. Control charts help monitor and assess economic model stability. The data used in this study is GDP data in Indonesia, Malaysia, Singapore, Brunei Darussalam, and Thailand, was collected from 1975 to 2021. This study discusses GSTAR model projections with actual GDP growth rate data to identify economic abnormalities in these linked countries. This research has major consequences for regional politicians, economists, and businesses. Policy decisions, investment strategies, and GSTAR model economic forecasts can benefit from understanding these countries' GDP growth interdependencies and patterns. Control chart analysis also assures the model accurately tracks economic trends over time. Finally, the GSTAR model and control chart analysis give a complete framework for modeling and testing allied GDP growth rates.
Referensi
[1] R. Jayathilaka et al., “Gross domestic product and logistics performance
index drive the world trade: A study based on all continents,”
PLoS One, vol. 17, no. 3, p. e0264474, Mar. 2022, doi: 10.1371/journal.
pone.0264474.
[2] Z. Yang, X. Zhang, J. Lei, Z. Duan, and J. Li, “Spatio-temporal Pattern
Characteristics of Relationship Between Urbanization and Economic
Development at County Level in China,” Chin Geogr Sci, vol. 29, no.
4, pp. 553–567, Aug. 2019, doi: 10.1007/s11769-019-1053-z.
[3] S. Zhao, D. Peng, H. Wen, and Y. Wu, “Nonlinear and spatial spillover
effects of the digital economy on green total factor energy efficiency:
evidence from 281 cities in China,” Environmental Science and Pollution
Research, vol. 30, no. 34, pp. 81896–81916, Aug. 2022, doi:
10.1007/s11356-022-22694-6.
[4] A. Phayal, A. Gold, and B. Prins, “Interstate hostility and maritime
crime: Evidence from South East Asia,” Mar Policy, vol. 143, p. 105134,
Sep. 2022, doi: 10.1016/j.marpol.2022.105134.
[5] S. Bi, “Cooperation between China and ASEAN under the building
of ASEAN Economic Community,” Journal of Contemporary
East Asia Studies, vol. 10, no. 1, pp. 83–107, Jan. 2021, doi:
10.1080/24761028.2021.1888410.
[6] W. Bai, L. Zhang, S. Lu, J. Ren, and Z. Zhou, “Sustainable energy
transition in Southeast Asia: Energy status analysis, comprehensive
evaluation and influential factor identification,” Energy, vol. 284, p.
128670, Dec. 2023, doi: 10.1016/j.energy.2023.128670.
[7] G. Ferrero, M. Pisani, and M. Tasso, “Policy Mix During a Pandemic
Crisis: A Review of the Debate on Monetary and Fiscal Responses and
the Legacy for the Future,” 2022, pp. 267–320. doi: 10.1007/978-3-031-
10302-5-11.
[8] A. Mohamad, I. M. Sifat, H. Mohd Thas Thaker, and A. M. Noor,
“On IMF debt and capital control: evidence from Malaysia, Thailand,
Indonesia, the Philippines and South Korea,” Journal of Financial
Regulation and Compliance, vol. 29, no. 2, pp. 143–162, May 2021,
doi: 10.1108/JFRC-08-2019-0108.
[9] H. Sun, B. K. Edziah, C. Sun, and A. K. Kporsu, “Institutional quality
and its spatial spillover effects on energy efficiency,” Socioecon Plann
Sci, vol. 83, p. 101023, Oct. 2022, doi: 10.1016/j.seps.2021.101023.
[10] Kriskkumar and Naseem, “Analysis of Oil Price Effect on Economic
Growth of ASEAN Net Oil Exporters,” Energies (Basel), vol. 12, no.
17, p. 3343, Aug. 2019, doi: 10.3390/en12173343.
[11] M. Murshed, R. Ahmed, C. Kumpamool, M. Bassim, and M. Elheddad,
“The effects of regional trade integration and renewable energy transition
on environmental quality: Evidence from South Asian neighbors,” Bus
Strategy Environ, vol. 30, no. 8, pp. 4154–4170, Dec. 2021, doi:
10.1002/bse.2862.
[12] S. Borovkova, H. P. Lopuha¨a, and B. N. Ruchjana, “Consistency and
Asymptotic Normality of Least Squares Estimators in Generalized STAR
Models,” Stat Neerl, vol. 62, no. 4, pp. 482–508, Nov. 2008, doi:
10.1111/j.1467-9574.2008.00391.x.
[13] N. M. Huda and N. Imro’ah, “Determination of the best weight matrix
for the Generalized Space Time Autoregressive (GSTAR) model in the
Covid-19 case on Java Island, Indonesia,” Spatial Statistics, vol. 54, p.
100734, Apr. 2023, doi: 10.1016/j.spasta.2023.100734.
[14] N. M. Huda, U. Mukhaiyar, and N. Imro’ah, “AN ITERATIVE PROCEDURE
FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL,”
BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 3, pp.
975–984, Sep. 2022, doi: 10.30598/barekengvol16iss3pp975-984.
[15] N. M. Huda, U. Mukhaiyar, and U. S. Pasaribu, “The Approximation
of GSTAR Model for Discrete Cases through INAR Model,” J Phys
Conf Ser, vol. 1722, no. 1, p. 012100, Jan. 2021, doi: 10.1088/1742-
6596/1722/1/012100.
[16] U. S. Pasaribu, U. Mukhaiyar, N. M. Huda, K. N. Sari, and S. W.
Indratno, “Modelling COVID-19 Growth Cases of Provinces in Java
Island by Modified Spatial Weight Matrix GSTAR through Railroad
Passenger’s Mobility,” Heliyon, vol. 7, no. 2, p. e06025, Feb. 2021, doi:
10.1016/j.heliyon.2021.e06025.
[17] U. Mukhaiyar, N. M. Huda, K. N. Sari, and U. S. Pasaribu, “Analysis
of Generalized Space Time Autoregressive with Exogenous Variable
(GSTARX) Model with Outlier Factor,” J Phys Conf Ser, vol. 1496,
no. 1, p. 012004, Mar. 2020, doi: 10.1088/1742-6596/1496/1/012004.
[18] U. Mukhaiyar, N. M. Huda, R. K. Novita Sari, and U. S. Pasaribu,
“Modeling Dengue Fever Cases by Using GSTAR(1;1) Model with
Outlier Factor,” J Phys Conf Ser, vol. 1366, no. 1, p. 012122, Nov.
2019, doi: 10.1088/1742-6596/1366/1/012122.
[19] Y. Yundari, N. M. Huda, U. S. Pasaribu, U. Mukhaiyar, and K. N. Sari,
“Stationary Process in GSTAR(1;1) through Kernel Function Approach,”
J Phys Conf Ser, p. 020010. doi: 10.1063/5.0016808.
[20] N. Imro’ah and N. M. Huda, “CONTROL CHART AS VERIFICATION
TOOLS IN TIME SERIES MODEL,” BAREKENG: Jurnal Ilmu
Matematika dan Terapan, vol. 16, no. 3, pp. 995–1002, Sep. 2022, doi:
10.30598/barekengvol16iss3pp995-1002.
[21] M. E. Kruk et al., “High-quality health systems in the Sustainable
Development Goals era: time for a revolution,” Lancet Glob Health,
vol. 6, no. 11, pp. e1196–e1252, Nov. 2018, doi: 10.1016/S2214-
109X(18)30386-3.
[22] N. Oktaviana and N. Amalia, “GROSS REGIONAL DOMESTIC
PRODUCT FORECASTS USING TREND ANALYSIS: CASE STUDY
OF BANGKA BELITUNG PROVINCE,” Jurnal Ekonomi & Studi
Pembangunan, vol. 19, no. 2, 2018, doi: 10.18196/jesp.19.2.5005.
[23] J. V. Henderson, A. Storeygard, and D. N. Weil, “Measuring Economic
Growth from Outer Space,” American Economic Review, vol. 102, no.
2, pp. 994–1028, Apr. 2012, doi: 10.1257/aer.102.2.994.
[24] Z. Zhao et al., “Analysis of the Spatial and Temporal Evolution of the
GDP in Henan Province Based on Nighttime Light Data,” Remote Sens
(Basel), vol. 15, no. 3, p. 716, Jan. 2023, doi: 10.3390/rs15030716.
[25] U. Mukhaiyar and U. S. Pasaribu, “A New Procedure of Generalized
STAR Modeling using IAcM Approach,” ITB Journal of Sciences, vol.
44, no. 2, pp. 179–192, 2012, doi: 10.5614/itbj.sci.2012.44.2.7.
[26] S. Borovkova, H. Lopuhaa, and B. Nurani, “Generalized STAR model
with experimental weights,” in 17th International Workshop on Statistical
Modelling, 2002, pp. 139–147.
[27] Yundari, U. S. Pasaribu, U. Mukhaiyar, and M. N. Heriawan, “Spatial
Weight Determination of GSTAR(1;1) Model by Using Kernel
Function,” J Phys Conf Ser, vol. 1028, p. 012223, Jun. 2018, doi:
10.1088/1742-6596/1028/1/012223.
[28] U. Mukhaiyar, B. I. Bilad, and U. S. Pasaribu, “The Generalized STAR
Modelling with Minimum Spanning Tree Approach of Weight Matrix
for COVID-19 Case in Java Island,” J Phys Conf Ser, vol. 2084, no. 1,
p. 012003, Nov. 2021, doi: 10.1088/1742-6596/2084/1/012003.
[29] B. N. Ruchjana, S. A. Borovkova, and H. P. Lopuhaa, “Least Squares
Estimation of Generalized Space Time AutoRegressive (GSTAR) Model
and Its Properties,” J Phys Conf Ser, pp. 61–64. doi: 10.1063/1.4724118.
[30] A. Faraz, W. H. Woodall, and C. Heuchenne, “Guaranteed conditional
performance of the S 2 control chart with estimated parameters,”
Int J Prod Res, vol. 53, no. 14, pp. 4405–4413, Jul. 2015, doi:
10.1080/00207543.2015.1008112.
[31] D. C. Montgomery and C. M. Borror, “Systems for modern quality and
business improvement,” Qual Technol Quant Manag, vol. 14, no. 4, pp.
343–352, Oct. 2017, doi: 10.1080/16843703.2017.1304032.
[32] M. Singh and R. Rathi, “A structured review of Lean Six Sigma in
various industrial sectors,” International Journal of Lean Six Sigma, vol.
10, no. 2, pp. 622–664, May 2019, doi: 10.1108/IJLSS-03-2018-0018.
[33] A. Almaya et al., “Control Strategies for Drug Product Continuous
Direct Compression—State of Control, Product Collection Strategies,
and Startup/Shutdown Operations for the Production of Clinical Trial
Materials and Commercial Products,” J Pharm Sci, vol. 106, no. 4, pp.
930–943, Apr. 2017, doi: 10.1016/j.xphs.2016.12.014.
[34] R. Sanchez-Marquez, J. M. Albarracin Guillem, E. Vicens-Salort, and J.
Jabaloyes Vivas, “A statistical system management method to tackle
data uncertainty when using key performance indicators of the balanced
scorecard,” J Manuf Syst, vol. 48, pp. 166–179, Jul. 2018, doi:
10.1016/j.jmsy.2018.07.010.
[35] K. Mukundam, D. R. N. Varma, G. R. Deshpande, V. Dahanukar, and
A. K. Roy, “I-MR Control Chart: A Tool for Judging the Health of
the Current Manufacturing Process of an API and for Setting the Trial
Control Limits in Phase I of the Process Improvement,” Org Process Res
Dev, vol. 17, no. 8, pp. 1002–1009, Aug. 2013, doi: 10.1021/op4001093.
[36] N. Mirzaei, S. Niroomand, and R. Zare, “Application of statistical process
control in service industry,” Journal of Modelling in Management,
vol. 11, no. 3, pp. 763–782, Aug. 2016, doi: 10.1108/JM2-06-2014-0046.
[37] H. Jeyabalan, L. M. Hee, and M. S. Leong, “Condition Monitoring of Industrial
Gas Turbine Critical Operating Parameters Using Statistical Process
Control Charts,” Applied Mechanics and Materials, vol. 773–774,
pp. 204–209, Jul. 2015, doi: 10.4028/www.scientific.net/AMM.773-
774.204.
[38] I. Bostan, C. Toma, G. Aevoae, I.-B. Robu, D. N. Mardiros, and
S,
tefan C. Topliceanu, “Effects of Internal and External Factors on
Economic Growth in Emerging Economies: Evidence from CEE Countries,”
East Europ Econ, vol. 61, no. 1, pp. 66–85, Jan. 2023, doi:
10.1080/00128775.2022.2109489.
