سنجش شکاف اعتباری در ایران: رویکرد نیمه‌ساختاری

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه اقتصاد، دانشکده اقتصاد، دانشگاه تهران، تهران، ایران

چکیده

در پاسخ به نقدهای وارد شده به روش‌های صرفاً آماری، در این مقاله بر اساس رویکرد نیمه‌ساختاری، شکاف اعتباری در اقتصاد ایران برای سال‌های 1373 تا 1398 محاسبه شده است. بدین منظور روند اعتبار بر پایه یک مدل همپوشانی نسلی به‌صورت تابعی از تولید بالقوه، نرخ بهره طبیعی، کیفیت نهادی و نسبت جمعیت جوان تصریح و سپس در قالب یک سیستم فضا-حالت روند و شکاف اعتباری تخمین زده شده است. نتایج نشان می‌دهد که در فاصله سال‌های 1383 تا 1387 و 1394 تا 1397 شکاف اعتباری مثبت قابل توجهی وجود دارد. به نظر می‌آید ریشه ایجاد رشد مازاد اعتباری در این دو دوره با یکدیگر متفاوت است. در بخشی از مطالعه با تجزیه اثرات، میزان تأثیر تغییرات هر یک از عوامل بر ایجاد شکاف اعتباری در بازه‌های مختلف محاسبه شده است. هم‌چنین بررسی بحران‌های مالی در ایران نشان داده است که شکاف اعتباری محاسبه شده، قدرت خوبی در پیش‌بینی بحران‌ها دارد.
طبقه بندی JEL: 
G21, E58, C32

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Measuring Credit Gap in Iran: Semi-Structural Approach

نویسندگان [English]

  • Ali Afzali
  • Ali Taiebnia
  • mohsen mehrara
Department of Economic, Faculty of Economic, University of Tehran, Tehran, Iran
چکیده [English]

In response to the criticisms of purely statistical methods, based on the semi-structural approach, the credit gap in Iran's economy was calculated from 1994 to 2019. For this purpose, the credit trend was specified based on a generational overlap model as a function of potential output, natural interest rate, institutional quality, and the ratio of the young population. Then, the trend and the credit gap were estimated as a state-space system. The results show a significant positive credit gap between 2013 to 2017 and 2014 to 2017. The origin of excessive credit growth in these two periods is different from each other. Also, the impact of structural variables changes on creating the credit gap in different periods was calculated. In addition, the study of financial crises in Iran reveals that this credit gap has good power in predicting crises.
JEL Classification: G21, E58, C32

کلیدواژه‌ها [English]

  • Banking facilities
  • Financial crisis
  • Semi-structure
  • State-space

مراجع

  1. عباسی نژاد، حسین و کاوند، حسین (1386). محاسبه معیاری برای بهره‌وری در ایران با استفاده از رهیافت کالمن فیلتر. پژوهش‌های اقتصادی ایران، 55-31.
  2. Abbasnejad, H. & Kavand, H. (2007). Calculation a productivity criterion in Iran by Kalman Filter approach. Iran Economic Researches.9(31).31-55. (In Persian).
  3. Afzali, A., Taiebnia, A., & Mehrara, M. (2022). Measuring Basel Credit Gap in Iran : Assessing Implications in Banking Supervision and Crises Prediction. Iranian Journal of Economic Studies. 10(1), 173–191. https://doi.org/10.22099/ijes.2021.41318.1772
  4. Baba, C., Erba, S. D., Detragiache, E., & Harrison, O. (2020). How Should Credit Gaps Be Measured ? — An Application to European Countries —. IMF Working Paper.
  5. Basel Committee on Banking Supervision. (2010). Guidance for national authorities operating the countercyclical capital buffer. In Report (Issue December). http://www.bis.org/publ/bcbs187.htm
  6. BIOLAN, B. C. (2018). a Comparison Between Detrending Methods: Hodrick-Prescott, Baxter-King, Christianofitzgerald Filters. a Short Survey. Review of the Air Force Academy, 16(2), 89–94. https://doi.org/10.19062/1842-9238.2018.16.2.10
  7. Blagrave, P., Garcia-Saltos, R., Laxton, D., & Zhang, F. (2015). A Simple Multivariate Filter for Estimating Potential Output. IMF Working Papers, 15(79), 1. https://doi.org/10.5089/9781475565133.001
  8. Borio, C., & Drehmann, M. (2009). Assessing the risk of banking crises – revisited. BIS Quarterly Review, March, 29–46. http://www.bis.org/publ/qtrpdf/r_qt0903e.pdf
  9. Borio, C., & Lowe, P. (2002). Assessing the risk of banking crises. BIS Quartlerly Review, December, 43–54. https://doi.org/10.1111/j.1523-1747.2005.0A153.x
  10. Detken, C., Weeken, O., Alessi, L., Bonfim, D., Boucinha, M. M., Castro, C., Frontczak, S., Giordana, G., Giese, J., Jahn, N., Kakes, J., Klaus, B., Lang, J. H., Puzanova, N., & Welz, P. (2014). Operationalising the Countercyclical Capital Buffer: Indicator Selection, Threshold Identification and Calibration Options. Ssrn, 5.
  11. Drehmann, M., Borio, C., Gambacorta, L., Jim\’{e}nez, G., & Trucharte, C. (2010). Countercyclical Capital Buffers: Exploring Options, Bank for International Settlements, Working Paper No. 317. Available at: www.bis.org.
  12. Drehmann, M., & Yetman, J. (2018). Why you should use the Hodrick-Prescott filter - at least to generate credit gaps. BIS Working Paper Series, 744. www.bis.org
  13. (2017). Financial Stability Review. Intellectual Property, 110(February), 1–190. http://www.eestipank.info/pub/en/dokumendid/publikatsioonid/seeriad/finantsvahendus/_2004_1/fsy_504.pdf
  14. Eggertsson, G. B., Mehrotra, N. R., & Robbins, J. A. (2019). A model of secular stagnation: Theory and quantitative evaluation. American Economic Journal: Macroeconomics, 11(1), 1–48. https://doi.org/10.1257/mac.20170367
  15. Elekdag, S., & Wu, Y. (2011). Rapid Credit Growth: Boon or Boom-Bust? In IMF Working Paper.
  16. Galán, J. E., & Mencia, J. (2018). Empirical Assessment of Alternative Structural Methods for Identifying Cyclical Systemic Risk in Europe. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3226820
  17. Hamilton, J. D. (2017). Nber Working Paper Series Why You Should Never Use the Hodrick-Prescott Filter. http://www.nber.org/papers/w23429
  18. Holston, K., Laubach, T., & Williams, J. C. (2016). Measuring the Natural Rate of Interest: International Trends and Determinants Board of Governors of the Federal Reserve System Measuring the Natural Rate of Interest: International Trends and Determinants *. Federal Reserve Bank of San Francisco Working Paper Series, 11. http://www.frbsf.org/economic-research/publications/working-
  19. (2014). Regional Economic Issues - Apr 2014. April.
  20. Innes, A. M. (1914). The credit theory of money. Credit and State Theories of Money: The Contributions of A. Mitchell Innes, 31, 50–78. https://doi.org/10.4337/9781843769842.00008
  21. Kaur, G. (2006). Munich Personal RePEc Archive Kalman Filter and its Economic Applications Kalman Filter and its Economic Applications. 22734.
  22. Lang, J., & Welz, P. (2018). Semi-structural credit gap estimation. ECB Working Paper, 2194.
  23. Laubach, T., & Williams, J. C. (2003). Measuring the natural rate of interest. Review of Economics and Statistics, 85(4), 1063–1070. https://doi.org/10.1162/003465303772815934
  24. Levine, R. (2005). Chapter 12 Finance and Growth: Theory and Evidence. Handbook of Economic Growth, 1(SUPPL. PART A), 865–934. https://doi.org/10.1016/S1574-0684(05)01012-9
  25. Reinhart, C., & Rogoff, K. (2009). International aspects of financial-market imperfections: The aftermath of financial crises. American Economic Review, 99(2), 466–472. https://doi.org/10.2139/ssrn.2882661
  26. Ruiz, A. U. (2015). Understanding the dichotomy of financial development : credit deepening versus credit excess. Bbva, 15/01(January), 1–43.
  27. Schularick, M., & Taylor, A. M. (2012). Credit booms gone bust: Monetary policy, leverage cycles, and financial crises, 1870-2008. American Economic Review, 102(2), 1029–1061. https://doi.org/10.1257/aer.102.2.1029
  28. Terrones, M., & Mendoza, E. (2004). Are credit booms in emerging markets a concern. World Economic Outlook, 147, 166.
  29. Werner, R. (2011). Economics as if banks mattered: A contribution based on the inductive methodology. Manchester School, 79(SUPPL.2), 25–35. https://doi.org/10.1111/j.1467-9957.2011.02265_5.x

فایل ها

سابقه مقاله

  • تاریخ دریافت: 06 اسفند 1401
  • تاریخ بازنگری: 28 مهر 1402
  • تاریخ پذیرش: 26 دی 1402

هم رسانی

ارجاع به این مقاله

آمار