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Skewed SVARs: Tracking the Structural Sources of Macroeconomic Tail Risks

In: Essays in Honour of Fabio Canova

Author

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  • Carlos Montes-Galdón
  • Eva Ortega

Abstract

This chapter proposes a vector autoregressive VAR model with structural shocks (SVAR) that are identified using sign restrictions, and whose distribution is subject to time varying skewness. The authors also present an efficient Bayesian algorithm to estimate the model. The model allows tracking joint asymmetric risks to macroeconomic variables included in the SVAR, and provides a structural narrative to the evolution of those risks. When faced with euro area data, our estimation suggests that there has been a significant variation in the skewness of demand, supply and monetary policy shocks. Such variation can explain a significant proportion of the joint dynamics of real GDP growth and inflation, and also generates important asymmetric tail risks in those macroeconomic variables. Finally, compared to the literature on growth- and inflation-at-risk, the authors find that financial stress indicators are not enough to explain all the macroeconomic tail risks.

Suggested Citation

  • Carlos Montes-Galdón & Eva Ortega, 2022. "Skewed SVARs: Tracking the Structural Sources of Macroeconomic Tail Risks," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 177-210, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-90532022000044a007
    DOI: 10.1108/S0731-90532022000044A007
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    1. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2008. "The new area-wide model of the euro area: a micro-founded open-economy model for forecasting and policy analysis," Working Paper Series 944, European Central Bank.
    2. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    3. Coenen, Günter & Karadi, Peter & Schmidt, Sebastian & Warne, Anders, 2018. "The New Area-Wide Model II: an extended version of the ECB’s micro-founded model for forecasting and policy analysis with a financial sector," Working Paper Series 2200, European Central Bank.
    4. Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
    5. Ciccarelli, Matteo & Osbat, Chiara, 2017. "Low inflation in the euro area: Causes and consequences," Occasional Paper Series 181, European Central Bank.
    6. Jonas E. Arias & Juan F. Rubio‐Ramírez & Daniel F. Waggoner, 2018. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications," Econometrica, Econometric Society, vol. 86(2), pages 685-720, March.
    7. Jesus Fernandez-Villaverde & Samuel Hurtado & Galo Nuno, 2019. "Financial Frictions and the Wealth Distribution," PIER Working Paper Archive 19-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    8. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    9. Chavleishvili, Sulkhan & Manganelli, Simone, 2019. "Forecasting and stress testing with quantile vector autoregression," Working Paper Series 2330, European Central Bank.
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    Cited by:

    1. Andrea Renzetti, 2023. "Modelling and Forecasting Macroeconomic Risk with Time Varying Skewness Stochastic Volatility Models," Papers 2306.09287, arXiv.org, revised Nov 2023.
    2. Deng, Chuang & Wu, Jian, 2023. "Macroeconomic downside risk and the effect of monetary policy," Finance Research Letters, Elsevier, vol. 54(C).
    3. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023. "Vector autoregression models with skewness and heavy tails," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    4. Montes-Galdón, Carlos & Paredes, Joan & Wolf, Elias, 2022. "Conditional density forecasting: a tempered importance sampling approach," Working Paper Series 2754, European Central Bank.
    5. Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2023. "Labour at risk," Working Paper Series 2840, European Central Bank.
    6. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2023. "What drives core inflation? The role of supply shocks," Working Paper Series 2875, European Central Bank.

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    More about this item

    Keywords

    Bayesian structural vector autoregressive; skewness; GDP risks; inflation risks; C11; C32; C51; E31; E32;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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