[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v42y2023i2p347-368.html
   My bibliography  Save this article

Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations

Author

Listed:
  • Tamás Kiss
  • Stepan Mazur
  • Hoang Nguyen
  • Pär Österholm
Abstract
In this paper, we analyze how skewness and heavy tails affect the estimated relationship between the real economy and the corporate bond‐yield spread—a popular predictor of real activity. We use quarterly US data to estimate Bayesian VAR models with stochastic volatility and various distributional assumptions regarding the innovations. In‐sample, we find that—after controlling for stochastic volatility—innovations in GDP growth can be well described by a Gaussian distribution. In contrast, the yield spread appears to benefit from being modeled using non‐Gaussian innovations. When it comes to real‐time forecasting performance, we find that the yield spread is a relevant predictor of GDP growth at the one‐quarter horizon. Having controlled for stochastic volatility, gains in terms of forecasting performance from flexibly modeling the innovations appear to be limited and are mostly found for the yield spread.

Suggested Citation

  • Tamás Kiss & Stepan Mazur & Hoang Nguyen & Pär Österholm, 2023. "Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 347-368, March.
  • Handle: RePEc:wly:jforec:v:42:y:2023:i:2:p:347-368
    DOI: 10.1002/for.2911
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2911
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.2911?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Giorgio Fagiolo & Mauro Napoletano & Andrea Roventini, 2008. "Are output growth-rate distributions fat-tailed? some evidence from OECD countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 639-669.
    2. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    3. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2017. "Microeconomic Origins of Macroeconomic Tail Risks," American Economic Review, American Economic Association, vol. 107(1), pages 54-108, January.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    5. Galbraith, John W. & van Norden, Simon, 2019. "Asymmetry in unemployment rate forecast errors," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1613-1626.
    6. 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.
    7. Ascari, Guido & Fagiolo, Giorgio & Roventini, Andrea, 2015. "Fat-Tail Distributions And Business-Cycle Models," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 465-476, March.
    8. Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2023. "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Journal of Econometrics, Elsevier, vol. 232(1), pages 52-69.
    9. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2024. "Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(5), pages 1099-1127, August.
    10. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    11. Gian Luigi Mazzi & James Mitchell & Gaetana Montana, 2014. "Density Nowcasts and Model Combination: Nowcasting Euro-Area GDP Growth over the 2008–09 Recession," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 233-256, April.
    12. Scott Brave & R. Andrew Butters, 2011. "Monitoring financial stability: a financial conditions index approach," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 35(Q I), pages 22-43.
    13. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    14. Jon Faust & Simon Gilchrist & Jonathan H. Wright & Egon Zakrajšsek, 2013. "Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1501-1519, December.
    15. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    16. Kiss, Tamás & Österholm, Pär, 2020. "Fat tails in leading indicators," Economics Letters, Elsevier, vol. 193(C).
    17. Esteban Prieto & Sandra Eickmeier & Massimiliano Marcellino, 2016. "Time Variation in Macro‐Financial Linkages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1215-1233, November.
    18. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
    19. Joshua C. C. Chan, 2017. "The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 17-28, January.
    20. Michael P. Clements & Ana Beatriz Galvão, 2013. "Real‐Time Forecasting Of Inflation And Output Growth With Autoregressive Models In The Presence Of Data Revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 458-477, April.
    21. Vasco Cúrdia & Marco Del Negro & Daniel L. Greenwald, 2014. "Rare Shocks, Great Recessions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1031-1052, November.
    22. Kjersti Aas & Ingrid Hobaek Haff, 2006. "The Generalized Hyperbolic Skew Student's t-Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 4(2), pages 275-309.
    23. Todd E. Clark, 2011. "Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 327-341, July.
    24. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed and Why?," NBER Chapters, in: NBER Macroeconomics Annual 2002, Volume 17, pages 159-230, National Bureau of Economic Research, Inc.
    25. Tilmann Gneiting & Roopesh Ranjan, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 411-422, July.
    26. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    27. Todd E. Clark & Francesco Ravazzolo, 2015. "Macroeconomic Forecasting Performance under Alternative Specifications of Time‐Varying Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 551-575, June.
    28. Liu, Xiaochun, 2019. "On tail fatness of macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 62(C).
    29. Florian Huber & Martin Feldkircher, 2019. "Adaptive Shrinkage in Bayesian Vector Autoregressive Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 27-39, January.
    30. Benjamin M. Friedman & Kenneth N. Kuttner, 1998. "Indicator Properties Of The Paper-Bill Spread: Lessons From Recent Experience," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 34-44, February.
    31. Karlsson, Sune & Mazur, Stepan, 2020. "Flexible Fat-tailed Vector Autoregression," Working Papers 2020:5, Örebro University, School of Business.
    32. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    33. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Financial conditions and density forecasts for US output and inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
    34. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    35. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    36. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    37. Steven Trypsteen, 2017. "The importance of time‐varying volatility and country interactions in forecasting economic activity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(6), pages 615-628, September.
    38. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    39. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    40. Acemoglu, Daron & Scott, Andrew, 1997. "Asymmetric business cycles: Theory and time-series evidence," Journal of Monetary Economics, Elsevier, vol. 40(3), pages 501-533, December.
    41. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    42. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    43. Kastner, Gregor & Frühwirth-Schnatter, Sylvia, 2014. "Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 408-423.
    44. 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).
    45. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pintér, Gábor, 2017. "Forecasting with VAR models: Fat tails and stochastic volatility," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1124-1143.
    46. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
    47. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    48. Hoang Nguyen & M Concepción Ausín & Pedro Galeano, 2019. "Parallel Bayesian Inference for High-Dimensional Dynamic Factor Copulas," Journal of Financial Econometrics, Oxford University Press, vol. 17(1), pages 118-151.
    49. Fountas, Stilianos & Karanasos, Menelaos, 2007. "Inflation, output growth, and nominal and real uncertainty: Empirical evidence for the G7," Journal of International Money and Finance, Elsevier, vol. 26(2), pages 229-250, March.
    50. Markus Brunnermeier & Darius Palia & Karthik A. Sastry & Christopher A. Sims, 2021. "Feedbacks: Financial Markets and Economic Activity," American Economic Review, American Economic Association, vol. 111(6), pages 1845-1879, June.
    51. Geert Bekaert & Alexander Popov, 2019. "On the Link Between the Volatility and Skewness of Growth," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 67(4), pages 746-790, December.
    52. 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.
    53. Creal, Drew D. & Tsay, Ruey S., 2015. "High dimensional dynamic stochastic copula models," Journal of Econometrics, Elsevier, vol. 189(2), pages 335-345.
    54. Karlsson, Sune & Österholm, Pär, 2020. "The relation between the corporate bond-yield spread and the real economy: Stable or time-varying?," Economics Letters, Elsevier, vol. 186(C).
    55. Kuttner, Kenneth N. & Friedman, Benjamin Morton, 1998. "Indicator Properties of the Paper—Bill Spread: Lessons from Recent Experience," Scholarly Articles 4554251, Harvard University Department of Economics.
    56. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    57. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    58. Gneiting, Tilmann & Ranjan, Roopesh, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 411-422.
    59. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-328, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Tamás Kiss & Hoang Nguyen & Pär Österholm, 2023. "Modelling Okun’s law: Does non-Gaussianity matter?," Empirical Economics, Springer, vol. 64(5), pages 2183-2213, May.
    3. Tamás Kiss & Hoang Nguyen & Pär Österholm, 2021. "Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails," JRFM, MDPI, vol. 14(11), pages 1-17, October.
    4. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    5. Kiss, Tamás & Nguyen, Hoang & Österholm, Pär, 2022. "The Relation between the High-Yield Bond Spread and the Unemployment Rate in the Euro Area," Finance Research Letters, Elsevier, vol. 46(PA).
    6. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    7. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    8. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    9. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2024. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," The Review of Economics and Statistics, MIT Press, vol. 106(5), pages 1403-1417, September.
    11. Fabian Krüger & Todd E. Clark & Francesco Ravazzolo, 2017. "Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 470-485, July.
    12. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2024. "Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(5), pages 1099-1127, August.
    13. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    14. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
    15. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper 2012/09, Norges Bank.
    16. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    17. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    18. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    19. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    20. Karlsson, Sune & Österholm, Pär, 2020. "The relation between the corporate bond-yield spread and the real economy: Stable or time-varying?," Economics Letters, Elsevier, vol. 186(C).

    More about this item

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:42:y:2023:i:2:p:347-368. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.