ANALISIS PENGARUH INDEKS MOBILITAS GOOGLE TERHADAP ‘MONEY SUPPLY’ DENGAN METODE ‘MIXED DATA SAMPLING (Studi Kasus Amerika Serikat dan Jepang)
DOI:
https://doi.org/10.25170/jrema.v2i2.5145Keywords:
Mixed Frequency Data Sampling, Google Mobility Report, Money Supply, Working Age Population, Polynomial Distributed LagAbstract
Pada masa pandemi COVID-19 memiliki potensi penularan virus Corona, untuk memutuskan rantai penyebaran virus tersebut maka banyak negara di hampir seluruh Dunia membatasi mobilitas di tempat-tempat tertentu. Google merilis data yang dikumpulkan dari mereka yang mengakses aplikasinya menggunakan perangkat seluler dan genggam. Google Community Mobility Reports atau mobilitas Google menunjukkan perubahan aktivitas dan mobilitas di berbagai jenis lokasi, dibandingkan dengan sebelum penyebaran COVID-19 secara global. Money supply (M1) menjadi indikator jumlah uang beredar di masyarakat, peneliti ingin mengetahui dengan adanya pengurangan mobilitas selama pandemi COVID-19 dalam kurun waktu 2020-2022 dapat mempengaruhi hal tersebut. Terdapat variable X tambahan yaitu working age population yang digunakan untuk mengukur total populasi pekerja dalam usia 15-64 tahun. Periode waktu pada indeks mobilitas Google adalah harian, sedangkan M1 dan populasi pekerja memiliki periode waktu bulanan.
Dalam penelitian ini, permasalahan penggabungan data dengan frekuensi yang berbeda dapat dijawab dengan menggunakan regresi metode Mixed Frequency Data Sampling (MIDAS) yang akan mengakomodasi perbedaan tersebut. Dalam persamaannya menggunakan model Polynomial Dsitributed Lag (PDL) untuk menjelaskan psanjang lag. Setiap model persamaan terbagi menjadi empat kategori yaitu level versus level, level versus change, change versus change, change versus level. Lalu, setiap kategori akan termuat tiga model persamaan. Selain menggunakan MIDAS, penetuan dalam menentukan model yang terbaik adalah memiliki probabilitas < 0.05, koefisien determinasi (R-squared) tertinggi, mencari Root Mean Squared Error (RMSE) terkecil, dan memiliki koefisien konstanta positif. Setelah dilakukannya regresi, dapat mengetahui pengaruh variabel independen terhadap variabel dependen.
Hasil penelitian menunjukkan bahwa regresi semua model yang ada pada kategori change versus level memiliki probabilitas 0.00 < 0.05. Pada model 1, dijelaskan bahwa adanya pengaruh mobilitas di area pemukiman terhadap M1. Pada model 2, dijelaskan adanya pengaruh mobilitas di area retail dan rekreasi terhadap M1. Dan, pada model 3 dijelaskan adanya pengaruh populasi pekerja terhadap M1
References
Afira, R., Prawiro, R., & Jamhur, A. I. (2023). VIRTUAL PRIVATE NETWORK (VPN) BASED ON IP SECURITY DESIGN ON TOPOLOGY. JOURNAL OF DYNAMICS (International Journal of Dynamics in Engineering and Sciences), 8(2), 56-59.
Alber, N., & Dabour, M. (2020). The Dynamic Relationship Between Fintech and Social Distancing Under COVID-19 Pandemic : Digital Payments Evidence. International Journal of Economics and Finance, 12(11). https://doi.org/10.5539/ijef.v12n11p109
Adesete, A., Mohammed, A. A., & Risikat, D. O. (2021). Financial innovation and economic growth: empirical evidence from Nigeria. EuroEconomica, 40(1).
Amassoma, D., Sunday, K., & Onyedikachi, E. E. (2018). The influence of money supply on inflation in Nigeria. Journal of Economics & Management, 31, 5-23.
Benazić, M. ‘NOWCASTING’ CROATIAN QUARTERLY REAL GDP WITH MONTHLY MONETARY AGGREGATE M1 DATA: A MIXED FREQUENCY APPROACH (MIDAS). Tourism, Innovations and Entrepreneurship TIE 2019, 365.
Bimo, I. D., Silalahi, E. E., & Kusumadewi, N. L. G. L. (2022). Corporate governance and investment efficiency in Indonesia: The moderating role of industry competition. Journal of Financial Reporting and Accounting, 20(2), 371-384
Botman, M. D. P., Anderson, M. D., & Hunt, M. B. L. (2014). Is Japan’ s Population Aging Deflationary? (No. 2014/139). International Monetary Fund.
Dharmastuti, C. F. (2016). Faktor Eksternal dan Internal yang Mempengaruhi Return Investasi Produk Reksa Dana Campuran di Indonesia. Media Ekonomi dan Manajemen, 29(2).
Dharmastuti, C. F., & Laurentxius, J. (2021). Factors and Benefits that Affect Lender's Interest in Giving Loans in Peer to Peer (P2P) Lending Platform. Binus Business Review, 12(2), 121-130.
Febrianti, V. D., & Saadah, S. (2023). Stock liquidity and stock returns: the moderating role of financial constraints. Journal of Accounting and Investment, 24(2), 292-305
Gousario, F., & Dharmastuti, C. F. (2015). Regional financial performance and human development index based on study in 20 counties/cities of level I region. The Winners, 16(2), 152-165.
Gujarati, D. N. 2006. Essentials of Econometrics. Third Edition. New York: The McGraw-Hill.
Hanani, R. T., & Dharmastuti, C. F. (2015). How do corporate governance mechanisms affect a firm’s potential for bankruptcy. Risk Governance and Control: Financial Markets and Institutions, 5(1), 61-71.
Handa, J. (2008). Monetary economics. Routledge.
Hervino, A. D., Insukindro, A. S. H., & Utami, S. (2023). Monetary Reaction Function in Indonesia During Inflation Targeting Period. Jurnal Ekonomi dan Studi Pembangunan, 15, 1
Karnadi, E. B., & Kusumahadi, T. A. (2021). Why Does Indonesia Have a High Covid-19 Case-Fatality Rate?. Jejak, 14(2), 272-287
Kusumahadi, T. A., & Permana, F. C. (2021). Impact of COVID-19 on global stock market volatility. Journal of Economic Integration, 36(1), 20-45
Lookman, K., Pujawan, N., & Nadlifatin, R. (2022). Measuring innovative capability maturity model of trucking companies in Indonesia. Cogent Business & Management, 9(1), 2094854.
Lookman K., Pujawan N., dan Nadlifatin R. (2023). Improving Innovative Capabilities of Trucking Company: Action Research Approach. Proceedings of the International Conference on Industrial Engineering and Operations Management Manila, Philippines, March 7-9, 2023
Maleva TM, Salmina AA, Grishina EE, Kartseva MA, Kuznetsova PO, Khasanova RR (2020) Evaluation of the effectiveness of antiepidemiological and social government measures in different regions – Northern Europe, Central Europe, Southern Europe, China, America. http://www.demoscope.ru/weekly/2020/0853/coronavirus01.php (in Russian)
Margaritha Sugianto, I., Pujawan, I. N., & Dwi Trijoyo Purnomo, J. (2022, January). Does Size Matter for Enhancing Company Resilience and Performance of Indonesian Trucking Company during COVID-19 Pandemic?. In 2022 The 3rd International Conference on Industrial Engineering and Industrial Management (pp. 72-78).
Marsudi, A. S., & Widjaja, Y. (2019). Industri 4.0 dan dampaknya terhadap financial technology serta kesiapan tenaga kerja di Indonesia. Ikraith Ekonomika, 2(2), 1–10. Retrieved from http://journals.upi-yai.ac.id/index.php/IKRAITH-EKONOMIKA/article/view/398
Marsudi, A. S. (2013). Analisis Sikap Pengguna e-commerce atas Privacy Sophistication Index (PSI) dan Implikasinya Pada e-entreprenuership. Proceeding Semnas {&} CFP Univ. Maranatha, 12. https://doi.org/10.17605/OSF.IO/J85HT
Monika, A. K. (2021). The utility of ‘COVID-19 mobility report’and ‘google trend’for analysing economic activities. Syntax Idea, 3(6), 1256-1268.
Nugroho, Y. D., & Kasuma, K. A. P. (2020). Analisis Perubahan Mobilitas Terhadap Proses Remediasi Dampak COVID-19 Di Indonesia Menggunakan Data Google Mobility. In Seminar Nasional Official Statistics (Vol. 2020, No. 1, pp. 344-348).
Nugroho, P. W., & Basuki, M. U. (2012). Analisis faktor-faktor yang mempengaruhi inflasi di Indonesia Periode 2000.1–2011.4 (Doctoral dissertation, Fakultas Ekonomika dan Bisnis).
Lin, Z., & Meissner, C. M. (2020). Health vs. wealth? public health policies and the economy during covid-19 (No. w27099). National Bureau of Economic Research.
Lukman, and Adewale, F. 2021. Almon KL Estimator for The Distributed Lag Model. Arab Journal of Basic and Applied Science Vol 28 Issue 1 : 406-412
Prasert, C., Kanchana, C., Chukiat, C., & Monekeo, K. (2015). Money supply influencing on Economic growth – wide phenomena of AEC open region. Procedia Economics and Finance, 24(2015), 108-115. https://doi.org/10.1016/S2212-5671(15)00626-7
Prawoto, N., Priyo Purnomo, E., & Az Zahra, A. (2020). The impacts of Covid-19 pandemic on socio-economic mobility in Indonesia.
Rufino, C. C. (2017). Nowcasting Philippine Economic Growth Using MIDAS Regression Modeling.
Balafif, S., & Aini, I. Q. (2022). Analysis of Computer Network Performance on Communication and Informatics Office of West Sumbawa Regency Using Quality of Service Method. Journal of Information Systems and Informatics, 4(4), 992-1007.
Safdari. M., Mehrizi. A. M., & Elahi. M. 2011. The Effect of Population Age Structure on Economic Growth in Iran. International Research Journal of Finance and Economics, 72 (1), 62-69
Sampi Bravo, J. R. E., & Jooste, C. (2020). Nowcasting economic activity in times of COVID-19: An approximation from the Google Community Mobility Report. World Bank Policy Research Working Paper, (9247).
Saddah, S., & Sitanggang, M. L. (2020). Value at risk estimation of exchange rate in banking industry. Jurnal Keuangan dan Perbankan, 24(4), 474-484
Santoso, W., Yusgiantoro, I., Soedarmono, W., & Prasetyantoko, A. (2021). The bright side of market power in Asian banking: Implications of bank capitalization and financial freedom. Research in International Business and Finance, 56, 101358
Utami, A. T., & Soebagiyo, D. (2013). Penentu inflasi di Indonesia; jumlah uang beredar, nilai tukar, ataukah cadangan devisa?. Jurnal Ekonomi & Studi Pembangunan, 14(2), 144-152.
Utari, D. T., & Ilma, H. (2018, October). Comparison of methods for mixed data sampling (MIDAS) regression models to forecast Indonesian GDP using agricultural exports. In AIP Conference Proceedings (Vol. 2021, No. 1, p. 060016). AIP Publishing LLC.
Utomo, F. G. R., & Saadah, S. (2022). Exchange Rate Volatility and Economic Growth: Managed Floating and Free-Floating Regime. Jurnal Keuangan dan Perbankan, 26(1), 173-183
Uyanto, S. S. (2020). Power comparisons of five most commonly used autocorrelation tests. Pakistan Journal of Statistics and Operation Research, 119-130
Van, D. D. (2019). Money supply and inflation impact on economic growth. Journal of Financial Economic Policy.
Wang, H., & Yamamoto, N. (2020). Using a partial differential Equation with Google Mobility data to predict COVID-19 in Arizona. arXiv preprint arXiv:2006.16928
