The Importance of the Autoregressive Distributed Lag (ARDL) Model in Econometric Modeling of Regional Socio-Economic Development
DOI:
https://doi.org/10.31150/ajebm.v8i7.3857Keywords:
Socio-Economic Development, Long-Term, Short-Term, ARDL, Autoregressive, GRP Per Capita, IP Per Capita, FCI Per Capita, ADF, PP, ARDL Bound TestAbstract
In this research work, the short- and long-term effects of independent variables such as industrial production per capita (IP per capita), fixed capital investment per capita (FCI per capita), and the unemployment rate on the dependent variable of gross regional product per capita (GRP per capita) were examined in the context of the Khorezm region in the Republic of Uzbekistan over the period from 2005 to 2024. The Autoregressive Distributed Lag (ARDL) model was employed to identify these effects. According to the findings, industrial production per capita and fixed capital investment per capita exert positive influences on gross regional product per capita in both the short and long term, whereas the unemployment rate demonstrates a negative impact in both periods.
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[1] International Monetary Fund (IMF), “World Economic Outlook: Policy Pivot, Rising Threats,” Washington, DC, October 2024, pp. 9-10. [Online]. Available: https://www.imf.org/en/Publications/WEO/Issues/2024/10/22/world-economic-outlook-october-2024.
[2] Sh. M. Mirziyoyev, Yangi Oʻzbekiston strategiyasi, Toshkent: “Oʻzbekiston,” pp. 131–170, 2021.
[3] M. Busu, “Analyzing the impact of the renewable energy sources on economic growth at the EU level using an ARDL model,” Mathematics, vol. 8, no. 8, p. 1367, 2020. [Online]. Available: https://doi.org/10.3390/math8081367.
[4] Q. Kong, D. Peng, Y. Ni, X. Jiang, and Z. Wang, “Trade openness and economic growth quality of China: Empirical analysis using ARDL model,” Fin. Res. Lett., vol. 38, 2021. [Online]. Available: https://doi.org/10.1016/j.frll.2020.101488.
[5] E. Iheanacho, “The impact of financial development on economic growth in Nigeria: An ARDL analysis,” Economies, vol. 4, no. 4, p. 26, 2016. [Online]. Available: https://doi.org/10.3390/economies4040026.
[6] A. Cherni and S. E. Jouini, “An ARDL approach to the CO2 emissions, renewable energy and economic growth nexus: Tunisian evidence,” Int. J. Hydrogen Energy, vol. 42, no. 48, 2017. [Online]. Available: https://doi.org/10.1016/j.ijhdene.2017.08.072.
[7] W. Bardi and M. A. Hfaiedh, “International trade and economic growth: evidence from a panel ARDL-PMG approach,” Int. Econ. Econ. Policy, vol. 18, pp. 847–868, 2021. [Online]. Available: https://doi.org/10.1007/s10368-021-00507-4.
[8] M. Qamruzzaman and J. Wei, “Financial innovation, stock market development, and economic growth: An application of ARDL model,” Int. J. Financ. Stud., vol. 6, no. 3, p. 69, 2018. [Online]. Available: https://doi.org/10.3390/ijfs6030069.
[9] M. M. Ageli, “Bootstrap ARDL on health expenditure, green energy, environmental sustainability, and economic growth nexus in Saudi Arabia,” Front. Environ. Sci., vol. 10, p. 993634, 2022. [Online]. Available: https://doi.org/10.3389/fenvs.2022.993634.
[10] T. H. Nguyen, B. Van Nguyen, T. T. Nguyen, and D. H. Luu, “Productivity unleashed: An ARDL model analysis of innovation and globalization effects,” J. Infrastruct. Policy Dev., vol. 8, no. 8, 2024. [Online]. Available: https://doi.org/10.24294/jipd.v8i8.5694.
[11] M. H. Pesaran and Y. Shin, “An autoregressive distributed lag modeling approach to cointegration analysis,” in Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium, S. Strom, Ed., Cambridge, U.K.: Cambridge University Press, 1999, ch. 11. [Online]. Available: https://doi.org/10.1017/CCOL521633230.011.
[12] M. H. Pesaran, Y. Shin, and R. J. Smith, “Bounds testing approaches to the analysis of level relationships,” J. Appl. Econ., vol. 16, pp. 289–326, 2001. [Online]. Available: https://doi.org/10.1002/jae.616.
[13] G. M. Ljung and G. P. E. Box, “On a measure of lack of fit in time series models,” Biometrika, vol. 65, pp. 297–303, 1978. [Online]. Available: https://doi.org/10.1093/biomet/65.2.297.
[14] D. A. Dickey and W. A. Fuller, “Likelihood ratio statistics for autoregressive time series with a unit root,” Econometrica, pp. 1057–1072, 1981. [Online]. Available: https://doi.org/10.2307/1912517.
[15] P. C. B. Phillips and P. Perron, “Testing for a unit root in time series regression,” Biometrika, vol. 75, no. 2, pp. 335–346, Jun. 1988. [Online]. Available: https://doi.org/10.1093/biomet/75.2.335.
[16] K. Neusser, Estimation of the Mean and the Autocorrelation Function, in Time Series Econometrics, Springer Texts in Business and Economics, Cham, Switzerland: Springer, 2016, pp. 67-85. [Online]. Available: https://doi.org/10.1007/978-3-319-32862-1_4.
[17] M. Verbeek, A Guide to Modern Econometrics, 5th ed., Hoboken, NJ: John Wiley & Sons, Inc., pp. 356-358, 2017.
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