Looking at GDP from a Statistical Perspective: Spatio-Temporal GSTAR(1;1) Model

Nur'ainul Miftahul Huda; Nurfitri Imro'ah; Nani Fitria Arini; Dewi Setyo Utami; Tarisa Umairah

doi:10.31764/jtam.v7i4.16236

Looking at GDP from a Statistical Perspective: Spatio-Temporal GSTAR(1;1) Model

Nur'ainul Miftahul Huda, Nurfitri Imro'ah, Nani Fitria Arini, Dewi Setyo Utami, Tarisa Umairah

Abstract

The gross domestic product (GDP) is a significant indicator for evaluating the performance of an economy. The GDP of a nation can be used to get a sense of the size and health of that nation's economy. Indonesia is the only nation from Southeast Asia to be represented in the G20. All G20’s countries play vital roles in creating the economic landscape of the region, the world, and everything in between. This research is focused on the increase of the GDP in Indonesia, Malaysia, Singapore, Thailand, and Brunei Darussalam. The spatial influence of GDP can be seen in the growth of each nation's infrastructure and industrial sector, for example. at the regional level, the increase of a country's GDP can also have an effect on the countries that are its neighbors. Using the GSTAR model, the aim of this study is to investigate the spatial and temporal influences on the GDP statistics of five different countries. The GSTAR model is distinguished by the presence of a weight matrix, which is one of its distinguishing features. In addition, the aim of this research is to select the most appropriate weight matrix for the purpose of representing the spatial effect on GDP statistics. Uniform, queen contiguity, and inverse distance weight matrices are the types of weight matrices that are utilized. Calculating each weight matrix, estimating relevant parameters, and performing diagnostic tests are the primary activities involved in this investigation. As a consequence of this, a weight matrix that is uniform in its distribution is the one that performs the best. The spatial and temporal correlations of GDP data may be accurately represented by the GSTAR model when it is equipped with a uniform weight matrix. This model is applied to five different countries.

Keywords

Spatio-temporal; Weight matrix.

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References

Aitken, A. (2019). Measuring Welfare Beyond GDP. National Institute Economic Review, 249, R3–R16. https://doi.org/10.1177/002795011924900110

Fan, V. Y., Bloom, D. E., Ogbuoji, O., Prettner, K., & Yamey, G. (2018). Valuing health as development: going beyond gross domestic product. BMJ, k4371. https://doi.org/10.1136/bmj.k4371

Hagerty, M. R., & Veenhoven, R. (2003). Wealth and Happiness Revisited – Growing National Income Does Go with Greater Happiness. Social Indicators Research, 64(1), 1–27. https://doi.org/10.1023/A:1024790530822

Hasan, R., & Barua, S. (2015). Financial Development and Economic Growth: Evidence from a Panel Study on South Asian Countries. Asian Economic and Financial Review, 5(10), 1159–1173.

Huda, N. M., & Imro’ah, N. (2023). 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, 54, 100734. https://doi.org/10.1016/j.spasta.2023.100734

Huda, N. M., Mukhaiyar, U., & Imro’ah, N. (2022). An Iterative Procedure For Outlier Detection In GSTAR (1;1) MODEL. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 16(3), 975–984. https://doi.org/10.30598/barekengvol16iss3pp975-984

Huda, N. M., Mukhaiyar, U., & Pasaribu, U. S. (2021). The Approximation of GSTAR Model for Discrete Cases through INAR Model. Journal of Physics: Conference Series, 1722(1), 012100. https://doi.org/10.1088/1742-6596/1722/1/012100

Kahoun, J., & Sixta, J. (2013). Regional GDP Compilation: Production, Income and Expediture Approach. Statistika: Statistics and Economy Journal, 93(4), 24–36.

Konchitchki, Y., & Patatoukas, P. N. (2014). Taking the Pulse of the Real Economy Using Financial Statement Analysis: Implications for Macro Forecasting and Stock Valuation. The Accounting Review, 89(2), 669–694. https://doi.org/10.2308/accr-50632

Mukhaiyar, U., Huda, N. M., Novita Sari, R. K., & Pasaribu, U. S. (2019). Modeling Dengue Fever Cases by Using GSTAR(1;1) Model with Outlier Factor. Journal of Physics: Conference Series, 1366(1), 012122. https://doi.org/10.1088/1742-6596/1366/1/012122

Mukhaiyar, U., & Pasaribu, U. S. (2012). A New Procedure of Generalized STAR Modeling using IAcM Approach. ITB Journal of Sciences, 44(2), 179–192. https://doi.org/10.5614/itbj.sci.2012.44.2.7

Nguyen, T. T., Pham, T. A. T., & Tram, H. T. X. (2020). Role of information and communication technologies and innovation in driving carbon emissions and economic growth in selected G-20 countries. Journal of Environmental Management, 261, 110162. https://doi.org/10.1016/j.jenvman.2020.110162

Onuoha, D. O., Ibe, A., Njoku, C. U., & Onuoha, J. I. (2015). Analysis of The Gross Domestic Product (G.D.P) of Nigeria:1960-2012. West African Journal of Industrial and Academic Research, 14(1).

Pasaribu, U. S., Mukhaiyar, U., Huda, N. M., Sari, K. N., & Indratno, S. W. (2021). Modelling COVID-19 Growth Cases of Provinces in Java Island by Modified Spatial Weight Matrix GSTAR through Railroad Passenger’s Mobility. Heliyon, 7(2), e06025. https://doi.org/10.1016/j.heliyon.2021.e06025

Pérez-Moreno, S., Rodríguez, B., & Luque, M. (2016). Assessing global competitiveness under multi-criteria perspective. Economic Modelling, 53, 398–408. https://doi.org/10.1016/j.econmod.2015.10.030

Ruchjana, B. N., Borovkova, S. A., & Lopuhaa, H. P. (2012). Least Squares Estimation of Generalized Space Time AutoRegressive (GSTAR) Model and Its Properties. 61–64. https://doi.org/10.1063/1.4724118

Saputra, F., & Hapzi Ali. (2021). The Impact of Indonesia’s Economic and Political Policy Regarding Participation in Various International Forums: G20 Forum (Literature Review of Financial Management). Journal of Accounting and Finance Management, 2(1), 40–51. https://doi.org/10.38035/jafm.v2i1.50

Wan, Y., & Sheng, N. (2022). Clarifying the relationship among green investment, clean energy consumption, carbon emissions, and economic growth: a provincial panel analysis of China. Environmental Science and Pollution Research, 29(6), 9038–9052. https://doi.org/10.1007/s11356-021-16170-w

Yundari, Pasaribu, U. S., Mukhaiyar, U., & Heriawan, M. N. (2018). Spatial Weight Determination of GSTAR(1;1) Model by Using Kernel Function. Journal of Physics: Conference Series, 1028, 012223. https://doi.org/10.1088/1742-6596/1028/1/012223

Yundari, Y., Huda, N. M., Pasaribu, U. S., Mukhaiyar, U., & Sari, K. N. (2020). Stationary Process in GSTAR(1;1) through Kernel Function Approach. 020010. https://doi.org/10.1063/5.0016808

DOI: https://doi.org/10.31764/jtam.v7i4.16236