Forecasting Macroeconomic Variables and their Effect on Poverty

Authors

  • Muhammad Ridhwan Assel Pattimura University
  • Bin Raudha Arif Hanoeboen Pattimura University
  • Abdul Aziz Laitupa Pattimura University
  • Fibryano Saptenno Pattimura University

DOI:

https://doi.org/10.14414/jebav.v25i3.3451

Keywords:

Macroeconomic Variables, Poverty, Bayesian VAR, Univariate Benchmark, ECM

Abstract

Forecasting macroeconomic variables is crucial to measure dynamic changes during uncertain economic conditions. This study examines and analyzes the appropriate and accurate forecasting model to predict macroeconomic variables in Maluku Province. The main variables used are economic growth, unemployment, inflation, and poverty. The modeling used in this study were Bayesian Vector Autoregressions Model and the Univariate Benchmark Model. The results of this study indicate that the two models have different specifications and forecasting directions. The value of the Univariate Benchmark model’s forecast error size is relatively smaller than that of the Bayesian Vector Autoregressions Model. The results of forecasting macroeconomic variables in Maluku Province have a relatively good level of accuracy and are close to the actual value of the sample period. The Error Correction Model test results show that only the Error Correction Term variable significantly affects the poverty level in the short term. Meanwhile, in the long term, the unemployment rate has a significant effect, and the model used is proven valid. The forecasting results from the model show that the Maluku provincial government must maintain the stability of macroeconomic variables, especially the inflation rate and unemploymentrate, because they tend to increase in the coming year. It can have an impact on reducing people’s purchasing power.

References

Azam, M., Haseeb, M., & Samsudin, S. (2016). The impact of foreign remittances on poverty alleviation: Global evidence. Economics and Sociology, 9(1), 264–281.

Baluga, A., & Nakane, M. (2020). Maldives Macroeconomics Forecasting a ComponentDriven Quarterly Bayesian Vector Autoregression Approach. Asian Development Bank, 78.

Bańbura, M., Giannone, D., & Lenza, M. (2015). Conditional forecasts and scenario analysis with vector autoregressions for large crosssections. International Journal of forecasting, 31(3), 739-756.

Baurle, G., E., Steiner., G., Z. (2018). Forecasting the production side of GDP Gregor Bäurle, Elizabeth Steiner and Gabriel Züllig Legal Issues Forecasting the production side of GDP ∗. SNB Working Papers, 16.

Carriero, A., Kapetanios, G., & Marcellino, M. (2009). Forecasting exchange rates with a large

Bayesian VAR. International Journal of Forecasting, 25(2), 400–417.

Chan, J. C. C., Jacobi, L., & Zhu, D. (2019). How Sensitive are VAR Forecast to Prior Hyperparameters? An Automated Sensitivity Analysis. Emerald Publishing, 40A, 229–248.

Clark, T. E., & Mccracken, M. W. (2014). VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A . Sims Article information. Emerald Publishing, 32, Issue 2013.

Cuong, N. V. (2011). Can Vietnam achieve the millennium development goal on poverty reduction in high inflation and economic stagnation? Developing Economies, 49(3), 297–320.

Datt, G., Ravallion, M., & Murgai, R. (2020). Poverty and Growth in India over Six Decades. American Journal of Agricultural Economics, 102(1), 4–27.

Diebold, F. X. (1998). The Past, Present, and Future of Macroeconomic Forecasting. Journal of Economic Perspectives, 12(2), 175–192.

Faharuddin, F., Yamin, M., Mulyana, A., & Yunita, Y. (2021). Impact of food price increases on poverty in Indonesia: empirical evidence from cross-sectional data. Journal of Asian Business and Economic Studies, (ahead-of-print)

Gao, F. (2021). China’s poverty alleviation “miracle†from the perspective of the structural transformation of the urban–rural dual economy. China Political Economy, 4(1), 86–109.

Gurkaynak, R. S., B. Kisacicoglu, B. R. (2014). VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims Article information: Emerald Group Publishing.

Harsmar, M. (2022). Agriculture, economic growth and poverty reduction. Working Paper. (Issue April).

Hill, H. (2021). What’s Happened to Poverty and Inequality in Indonesia over Half a Century? Asian Development Review, 38(1), 68–97.

Hoover, G. A., Enders, W., & Freeman, D. G. (2008). Non-white poverty and macroeconomy: The impact of growth. American Economic Review, 98(2), 398–402.

Insukindro. (1999). Selection of an empirical economic model using an error correction approach, Indonesian Journal of Economics and Business, 14(31), pp. 1–13.

International Monetary Fund. (2022). World Economic Outlook Update: Rising Caseloads, a Disrupted Recovery, and Higher Inflation.

Iyer, T., & Gupta, A. Sen. (2019). Quarterly Forecasting Model for India’s Economic Growth: Bayesian Vector Autoregression Approach ADB Economics Working Papers. 573.

Juhro, S. M., & Iyke, B. N. (2019). Forecasting Indonesia inflation within an inflation-targeting framework: do large-scale models pay off? Bulletin of Monetary Economics and Banking, 22(4), 423–436.

Kusumaningsih, M., Setyowati, E., & Ridhwan, H. R. (2022). Study on the impact of economic growth, unemployment, and education on South Kalimantan Province’s poverty level from 2014 to 2020. 655(Icoebs), 170–177.

Miranda, S., Agrippino, S., & Ricco, G. (2018). Bayesian Vector Autoregression. Sciences Po Ofce Working Papers. https://www.ofce.sciencespo.fr/pdf/dtravail/OFCEWP2018-18.pdf

Miranda, S., Agrippino, A. G. R. (2018). Bayesian Vector Autoregressions. Oxford Encyclopedia of Economics and Finance.

Musakwa, M. T., & Odhiambo, N. M. (2019). FDI and poverty reduction in Botswana: A multivariate causality test. Economics and Sociology, 12(3), 54–66.

Nugroho, D., Asmanto, P., Adji, A., & Hidayat, T. (2020). Leading Indicators of poverty in Indonesia: Application in the Short-Term Outlook. Working Papers, 49 (Issue July).

Nugraha, N., Kamio, K., & Gunawan, D. S. (2021). Faktor-Faktor penyebab utang luar negeri dan dampaknya terhadap pertumbuhan ekonomi Indonesia. Jurnal Ilmiah Universitas Batanghari Jambi, 21(1), 21-26.

Pham, T. H., & Riedel, J. (2019). Impacts of the sectoral composition of growth on poverty reduction in Vietnam. Journal of Economics and Development, 21(2), 213-222.

Sasmal, R. & J. S. (2016). Public expenditure, economic growth and poverty alleviation. International Journal of Social Economics, 43(6), 604–618.

Siyan, P., Adegoriola, A. E., & Adolphus, J. A. (2016). Munich Personal RePEc Archive Unemployment and Inflation: Implication on Poverty Level in Nigeria. Munich Personal RePEc Archive, 79765, 1–23. https://mpra.ub.unimuenchen.de/79765/1/MPRA_paper_79765.pdf

Soltero, J. M. (2020). Economic Sector Employment, Human Capital, and Poverty among Mexican Immigrants in Chicago. Journal of Poverty, 24(4), 318–333.

Sugita, K. (2022). Forecasting with Bayesian vector autoregressive models: comparison of direct and iterated multistep methods. Asian Journal of Economics and Banking (AJEB), 6(2), 142-154.

Teguh, M., & Bashir, A. (2019). Indonesia’s Economic Growth Forecasting. SIJDEB, 3(1), 134–145.

Vanegas, M. (2014). The triangle of poverty, economic growth, and inequality in Central America: does tourism matter?. Worldwide Hospitality and Tourism Themes, 6(3), 277-292.

World Bank Group. (2020). Poverty and Shared Prosperity 2020. https://www.worldbank.org/en/publication/poverty-and-shared-prosperity

Zhu, Y., Bashir, S., & Marie, M. (2022). Assessing the Relationship between Poverty and Economic Growth: Does Sustainable Development Goal Can be Achieved? Environmental Science and Pollution Research, 29, 27613–27623.

Downloads

Published

2023-03-21

How to Cite

Assel, M. R., Hanoeboen, B. R. A., Laitupa, A. A., & Saptenno, F. (2023). Forecasting Macroeconomic Variables and their Effect on Poverty. Journal of Economics, Business, and Accountancy Ventura, 25(3), 322–337. https://doi.org/10.14414/jebav.v25i3.3451