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The Growth-at-Risk (GaR) Framework: Implication For Ukraine

Author

Listed:
  • Anastasiya Ivanova

    (National Bank of Ukraine)

  • Alona Shmygel

    (National Bank of Ukraine)

  • Ihor Lubchuk

    (National Bank of Ukraine)

Abstract

Using data for the Ukrainian economy, we applied and adapted the growth-at-risk (GaR) framework to examine the association between financial conditions, credit and sectors' activity, and external conditions and the probability distribution of GDP growth in Ukraine. We applied CSA and PCA approaches to construct indices of these partitions. We further derived GDP growth distributions and explored their behavior under different scenarios. Results from the model with PCA indices suggest that the relationships between financial conditions as well as external conditions indices and economic activity are inverse regardless of quantile of GDP distribution. Moreover, we found that the financial conditions index has the largest effect on the GDP growth on the lower quantiles, which could generate significant downside risk to the economy.

Suggested Citation

  • Anastasiya Ivanova & Alona Shmygel & Ihor Lubchuk, 2021. "The Growth-at-Risk (GaR) Framework: Implication For Ukraine," IHEID Working Papers 10-2021, Economics Section, The Graduate Institute of International Studies.
  • Handle: RePEc:gii:giihei:heidwp10-2021
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    References listed on IDEAS

    as
    1. Martina Hengge, 2019. "Uncertainty as a Predictor of Economic Activity," IHEID Working Papers 19-2019, Economics Section, The Graduate Institute of International Studies.
    2. Mr. Ananthakrishnan Prasad & Mr. Selim A Elekdag & Mr. Phakawa Jeasakul & Romain Lafarguette & Mr. Adrian Alter & Alan Xiaochen Feng & Changchun Wang, 2019. "Growth at Risk: Concept and Application in IMF Country Surveillance," IMF Working Papers 2019/036, International Monetary Fund.
    3. Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021. "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, vol. 101(C).
    4. Yan Wang & Yudong Yao, 2001. "Measuring economic downside risk and severity - Growth at Risk," Policy Research Working Paper Series 2674, The World Bank.
    5. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    6. repec:ces:ifodic:v:15:y:2017:i:1:p:19307486 is not listed on IDEAS
    7. International Monetary Fund, 2018. "Peru: Financial System Stability Assessment," IMF Staff Country Reports 2018/238, International Monetary Fund.
    8. Stephan Kohns, 2017. "Monetary Policy and Financial Stability," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 15(1), pages 17-18, 04.
    9. Lang, Jan Hannes & Forletta, Marco, 2019. "Bank capital-at-risk: measuring the impact of cyclical systemic risk on future bank losses," Macroprudential Bulletin, European Central Bank, vol. 9.
    10. Tobias Adrian & Federico Grinberg & Nellie Liang & Sheheryar Malik & Jie Yu, 2022. "The Term Structure of Growth-at-Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(3), pages 283-323, July.
    11. Stephan Kohns, 2017. "Monetary Policy and Financial Stability," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 15(01), pages 17-18, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Quantile regression; economic growth; GDP; principal component analysis; GDP growth distribution;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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