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Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs

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  • Javier Sánchez García

    (Mediterranean Research Center for Economics and Sustainable Development (CIMEDES), Universidad de Almería, 04120 Almería, Spain)

  • Salvador Cruz Rambaud

    (Departamento de Economía y Empresa, Universidad de Almería, 04120 Almería, Spain)

Abstract

Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include multiple variables and lags which, however, turns into an exponential growth of the parameters to be estimated. This means that high-dimensional models with multiple variables and lags are difficult to estimate, leading to omitted variables, information biases and a loss of potential forecasting power. Traditionally, the existing literature has resorted to factor analysis, and specially, to Bayesian methods to overcome this situation. This paper explores the so-called machine learning regularization methods as an alternative to traditional methods of forecasting and impulse response analysis. We find that regularization structures, which allow for high dimensional models, perform better than standard Bayesian methods in nowcasting and forecasting. Moreover, impulse response analysis is robust and consistent with economic theory and evidence, and with the different regularization structures. Specifically, regarding the best regularization structure, an elementwise machine learning structure performs better in nowcasting and in computational efficiency, whilst a componentwise structure performs better in forecasting and cross-validation methods.

Suggested Citation

  • Javier Sánchez García & Salvador Cruz Rambaud, 2022. "Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:877-:d:767891
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    as
    1. Boivin, Jean & Kiley, Michael T. & Mishkin, Frederic S., 2010. "How Has the Monetary Transmission Mechanism Evolved Over Time?," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 8, pages 369-422, Elsevier.
    2. Fabio Canova & Matteo Ciccarelli, 2009. "Estimating Multicountry Var Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 929-959, August.
    3. Haldrup, Niels & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2010. "A vector autoregressive model for electricity prices subject to long memory and regime switching," Energy Economics, Elsevier, vol. 32(5), pages 1044-1058, September.
    4. Ben S. Bernanke & Mark Gertler, 1995. "Inside the Black Box: The Credit Channel of Monetary Policy Transmission," Journal of Economic Perspectives, American Economic Association, vol. 9(4), pages 27-48, Fall.
    5. Claeys, Peter & Vašíček, Bořek, 2014. "Measuring bilateral spillover and testing contagion on sovereign bond markets in Europe," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 151-165.
    6. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    7. Bernanke, Ben S & Blinder, Alan S, 1992. "The Federal Funds Rate and the Channels of Monetary Transmission," American Economic Review, American Economic Association, vol. 82(4), pages 901-921, September.
    8. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    9. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
    10. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    11. Florio, Anna, 2018. "Nominal anchors and the price puzzle," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 224-237.
    12. Nick, Sebastian & Thoenes, Stefan, 2014. "What drives natural gas prices? — A structural VAR approach," Energy Economics, Elsevier, vol. 45(C), pages 517-527.
    13. William Cheung & Scott Fung & Shih-Chuan Tsai, 2010. "Global capital market interdependence and spillover effect of credit risk: evidence from the 2007-2009 global financial crisis," Applied Financial Economics, Taylor & Francis Journals, vol. 20(1-2), pages 85-103.
    14. Estrella, Arturo, 2015. "The Price Puzzle And Var Identification," Macroeconomic Dynamics, Cambridge University Press, vol. 19(8), pages 1880-1887, December.
    15. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    16. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    17. Diaz, Elena Maria & Molero, Juan Carlos & Perez de Gracia, Fernando, 2016. "Oil price volatility and stock returns in the G7 economies," Energy Economics, Elsevier, vol. 54(C), pages 417-430.
    18. Yunsun Kim & Sahm Kim, 2021. "Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models," Mathematics, MDPI, vol. 9(18), pages 1-15, September.
    19. Feltenstein, Andrew & Iwata, Shigeru, 2005. "Decentralization and macroeconomic performance in China: regional autonomy has its costs," Journal of Development Economics, Elsevier, vol. 76(2), pages 481-501, April.
    20. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
    21. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575.
    22. Fenghua Wen & Feng Min & Yue‐Jun Zhang & Can Yang, 2019. "Crude oil price shocks, monetary policy, and China's economy," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 812-827, April.
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