Modeling and Forecasting Macroeconomic Downside Risk

References

• Adrian, T., Boyarchenko, N., and Giannone, D. (2019), “Vulnerable Growth,” American Economic Review, 109, 1263–89. DOI: 10.1257/aer.20161923.
   Web of Science ®Google Scholar
• Adrian, T., Duarte, F., Liang, N., and Zabczyk, P. (2020), “NKV: A New Keynesian Model with Vulnerability,” AEA Papers and Proceedings, 110, 470–76. DOI: 10.1257/pandp.20201023.
   Web of Science ®Google Scholar
• Adrian, T., Grinberg, F., Liang, N., Malik, S., and Yu, J. (2022), “The Term Structure of Growth-at-Risk,” American Economic Journal: Macroeconomics, Forthcoming. DOI: 10.1257/mac.20180428.
   Web of Science ®Google Scholar
• Adrian, T., and Shin, H. S. (2008), “Financial Intermediaries, Financial Stability and Monetary Policy,” Proceedings - Economic Policy Symposium - Jackson Hole, pp. 287–334.
   Google Scholar
• Alessi, L., Ghysels, E., Onorante, L., Peach, R., and Potter, S. (2014), “Central Bank Macroeconomic Forecasting during the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences,” Journal of Business & Economic Statistics, 32, 483–500. DOI: 10.1080/07350015.2014.959124.
   Web of Science ®Google Scholar
• Antolin-Diaz, J., Drechsel, T., and Petrella, I. (2017), “Tracking the Slowdown in Long-Run GDP Growth,” Review of Economics and Statistics, 99, 343–356. DOI: 10.1162/REST_a_00646.
   Web of Science ®Google Scholar
• Arellano-Valle, R. B., Gómez, H. W., and Quintana, F. A. (2005), “Statistical Inference for a General Class of Asymmetric Distributions,” Journal of Statistical Planning and Inference, 128, 427–443. DOI: 10.1016/j.jspi.2003.11.014.
   Web of Science ®Google Scholar
• Azzalini, A., and Capitanio, A. (2003), “Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skew t-distribution,” Journal of the Royal Statistical Society, Series B, 65, 367–389. DOI: 10.1111/1467-9868.00391.
   Google Scholar
• Bai, J., and Ng, S. (2005), “Tests for Skewness, Kurtosis, and Normality for Time Series Data,” Journal of Business & Economic Statistics, 23, 49–60. DOI: 10.1198/073500104000000271.
   Web of Science ®Google Scholar
• Bekaert, G., and Engstrom, E. (2017), “Asset Return Dynamics under Habits and Bad Environment-Good Environment Fundamentals,” Journal of Political Economy, 125, 713–760. DOI: 10.1086/691450.
   Web of Science ®Google Scholar
• Belloni, A., and Chernozhukov, V. (2009), “On the Computational Complexity of MCMC-Based Estimators in Large Samples,” The Annals of Statistics, 37, 2011–2055. DOI: 10.1214/08-AOS634.
   Web of Science ®Google Scholar
• Berkowitz, J. (2001), “Testing Density Forecasts, With Applications to Risk Management,” Journal of Business & Economic Statistics, 19, 465–474. DOI: 10.1198/07350010152596718.
   Web of Science ®Google Scholar
• Besag, J., Green, P., Higdon, D., and Mengersen, K. (1995), “Bayesian Computation and Stochastic Systems,” Statistical Science, 10, 3–41. DOI: 10.1214/ss/1177010123.
   Web of Science ®Google Scholar
• Blasques, F., Gorgi, P., Koopman, S. J., and Wintenberger, O. (2018), “Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models,” Electronic Journal of Statistics, 12, 1019–1052. DOI: 10.1214/18-EJS1416.
   Web of Science ®Google Scholar
• Blasques, F., Koopman, S. J., and Lucas, A. (2015), “Information-Theoretic Optimality of Observation-Driven Time Series Models for Continuous Responses,” Biometrika, 102, 325–343. DOI: 10.1093/biomet/asu076.
   Web of Science ®Google Scholar
• Blasques, F., van Brummelen, J., Koopman, S. J., and Lucas, A. (2022), “Maximum Likelihood Estimation for Score-Driven Models,” Journal of Econometrics, 227, 325–346. DOI: 10.1016/j.jeconom.2021.06.003.
   Web of Science ®Google Scholar
• Brave, S., and Butters, R. A. (2012), “Diagnosing the Financial System: Financial Conditions and Financial Stress,” International Journal of Central Banking, 8, 191–239.
   Web of Science ®Google Scholar
• Brownlees, C., and Souza, A. B. (2021), “Backtesting Global Growth-at-Risk,” Journal of Monetary Economics, 118, 312–330. DOI: 10.1016/j.jmoneco.2020.11.003.
   Web of Science ®Google Scholar
• Byrd, R. H. (1978), “Local Convergence of the Diagonalized Method of Multipliers,” Journal of Optimization Theory and Applications, 26, 485–500. DOI: 10.1007/BF00933148.
   Web of Science ®Google Scholar
• Carriero, A., Clark, T. E., and Marcellino, M. (Forthcoming), “Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions,” Journal of Money, Credit and Banking.
   Google Scholar
• Cecchetti, S. G. (2008), “Measuring the Macroeconomic Risks Posed by Asset Price Booms,” in Asset Prices and Monetary Policy, University of Chicago Press, NBER Chapters, 9–43.
   Google Scholar
• Chauvet, M., and Potter, S. (2013), “Forecasting Output,” in Handbook of Economic Forecasting (Vol. 2), pp. 141–194, Amsterdam: Elsevier.
   Google Scholar
• Clark, T. E., and Ravazzolo, F. (2015), “Macroeconomic Forecasting Performance under Alternative Specifications of Time-Varying Volatility,” Journal of Applied Econometrics, 30, 551–575. DOI: 10.1002/jae.2379.
   Web of Science ®Google Scholar
• Cox, D. R. (1981), “Statistical Analysis of Time Series: Some Recent Developments,” Scandinavian Journal of Statistics, 8, 93–115.
   Web of Science ®Google Scholar
• Creal, D., Koopman, S. J., and Lucas, A. (2013), “Generalized Autoregressive Score Models with Applications,” Journal of Applied Econometrics, 28, 777–795. DOI: 10.1002/jae.1279.
   Web of Science ®Google Scholar
• Delle Monache, D., and Petrella, I. (2017), “Adaptive Models and Heavy Tails with an Application to Inflation Forecasting,” International Journal of Forecasting, 33, 482–501. DOI: 10.1016/j.ijforecast.2016.11.007.
   Web of Science ®Google Scholar
• Dennis, J. E., and Schnabel, R. B. (1996), Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Philadelphia, PA: Society for Industrial and Applied Mathematics.
   Google Scholar
• Diebold, F. X., and Mariano, R. S. (1995), “Comparing Predictive Accuracy,” Journal of Business & Economic Statistics, 20, 134–144. DOI: 10.1198/073500102753410444.
   Web of Science ®Google Scholar
• Doz, C., Ferrara, L., and Pionnier, P.-A. (2020), “Business Cycle Dynamics After the Great Recession: An Extended Markov-Switching Dynamic Factor Model,” OECD Working Papers 2020/01.
   Google Scholar
• Engle, R. F., and Lee, G. (1999), “A Permanent and Transitory Component Model of Stock Return Volatility,” in Causality, and Forecasting: A Festschrift in Honor of Clive W. J. Granger, eds. R. F. Engle and H. White, pp. 475–497, Oxford: Oxford University Press.
   Google Scholar
• Engle, R. F., and Rangel, J. G. (2008), “The Spline-GARCH Model for Low-Frequency Volatility and its Global Macroeconomic Causes,” The Review of Financial Studies, 21, 1187–1222. DOI: 10.1093/rfs/hhn004.
   Web of Science ®Google Scholar
• Eo, Y., and Morley, J. (2022), “Why Has the U.S. Economy Stagnated since the Great Recession?” The Review of Economics and Statistics, 104, 246–258. DOI: 10.1162/rest_a_00957.
   Web of Science ®Google Scholar
• Fernández-Villaverde, J., and Guerrón-Quintana, P. (2020), “Estimating DSGE Models: Recent Advances and Future Challenges,” NBER Working Papers 27715.
   Google Scholar
• Fissler, T., Ziegel, J. F., and Gneiting, T. (2016), “Expected Shortfall is Jointly Elicitable with Value-at-Risk: Implications for Backtesting,” Risk.net.
   Google Scholar
• Ganics, G., Rossi, B., and Sekhposyan, T. (2020), “From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Forecasts,” CEPR Discussion Papers 14267, Centre for Economic Policy Research.
   Google Scholar
• Gertler, M., and Gilchrist, S. (2018), “What Happened: Financial Factors in the Great Recession,” Journal of Economic Perspectives, 32, 3–30. DOI: 10.1257/jep.32.3.3.
   Web of Science ®Google Scholar
• Giacomini, R., and Komunjer, I. (2005), “Evaluation and combination of conditional quantile forecasts,” Journal of Business & Economic Statistics, 23, 416–431. DOI: 10.1198/073500105000000018.
   Web of Science ®Google Scholar
• Giglio, S., Kelly, B., and Pruitt, S. (2016), “Systemic Risk and the Macroeconomy: An Empirical Evaluation,” Journal of Financial Economics, 119, 457–471. DOI: 10.1016/j.jfineco.2016.01.010.
   Web of Science ®Google Scholar
• Gneiting, T., and Raftery, A. E. (2007), “Strictly Proper Scoring Rules, Prediction, and Estimation,” Journal of the American Statistical Association, 102, 359–378. DOI: 10.1198/016214506000001437.
   Web of Science ®Google Scholar
• Gneiting, T., and Ranjan, R. (2011), “Comparing Density Forecasts Using Threshold-and Quantile-Weighted Scoring Rules,” Journal of Business & Economic Statistics, 29, 411–422. DOI: 10.1198/jbes.2010.08110.
   Web of Science ®Google Scholar
• Gómez, H. W., Torres, F. J., and Bolfarine, H. (2007), “Large-Sample Inference for the Epsilon-Skew-t Distribution,” Communications in Statistics-Theory & Methods, 36, 73–81. DOI: 10.1080/03610920600966514.
   Web of Science ®Google Scholar
• Haario, H., Saksman, E., and Tamminen, J. (1999), “Adaptive Proposal Distribution for Random Walk Metropolis Algorithm,” Computational Statistics, 14, 375–396. DOI: 10.1007/s001800050022.
   Web of Science ®Google Scholar
• Hahn, P. R., and Carvalho, C. M. (2015), “Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective,” Journal of the American Statistical Association, 110, 435–448. DOI: 10.1080/01621459.2014.993077.
   Web of Science ®Google Scholar
• Hansen, B. E. (1994), “Autoregressive Conditional Density Estimation,” International Economic Review, 35, 705–730. DOI: 10.2307/2527081.
   Web of Science ®Google Scholar
• Harvey, A., and Luati, A. (2014), “Filtering With Heavy Tails,” Journal of the American Statistical Association, 109, 1112–1122. DOI: 10.1080/01621459.2014.887011.
   Web of Science ®Google Scholar
• Harvey, A. C. (2013), Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series (Vol. 52), Cambridge: Cambridge University Press.
   Google Scholar
• Harvey, C. R., and Siddique, A. (1999), “Autoregressive Conditional Skewness,” Journal of Financial and Quantitative Analysis, 34, 465–487. DOI: 10.2307/2676230.
   Web of Science ®Google Scholar
• Harvey, D., Leybourne, S., and Newbold, P. (1997), “Testing the Equality of Prediction Mean Squared Errors,” International Journal of Forecasting, 13, 281–291. DOI: 10.1016/S0169-2070(96)00719-4.
   Web of Science ®Google Scholar
• Huber, F., Koop, G., and Onorante, L. (2021), “Inducing Sparsity and Shrinkage in Time-Varying Parameter Models,” Journal of Business & Economic Statistics, 39, 669–683. DOI: 10.1080/07350015.2020.1713796.
   Web of Science ®Google Scholar
• Jensen, H., Petrella, I., Ravn, S. H., and Santoro, E. (2020), “Leverage and Deepening Business-Cycle Skewness,” American Economic Journal: Macroeconomics, 12, 245–281. DOI: 10.1257/mac.20170319.
   Web of Science ®Google Scholar
• Jordà, Ò., Schularick, M., and Taylor, A. M. (2013), “When Credit Bites Back,” Journal of Money, Credit and Banking, 45, 3–28. DOI: 10.1111/jmcb.12069.
   Web of Science ®Google Scholar
• Jurado, K., Ludvigson, S. C., and Ng, S. (2015), “Measuring Uncertainty,” American Economic Review, 105, 1177–1216. DOI: 10.1257/aer.20131193.
   Web of Science ®Google Scholar
• Koopman, S. J., Lucas, A., and Scharth, M. (2016), “Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models,” Review of Economics and Statistics, 98, 97–110. DOI: 10.1162/REST_a_00533.
   Web of Science ®Google Scholar
• Koopman, S. J., Lucas, A., and Zamojski, M. (2018), “Dynamic Term Structure Models with Score-Driven Time-Varying Parameters,” WP 258, Narodowy Bank Polski.
   Google Scholar
• McConnell, M. M., and Perez-Quiros, G. (2000), “Output Fluctuations in the United States: What Has Changed Since the Early 1980’s?” American Economic Review, 90, 1464–1476. DOI: 10.1257/aer.90.5.1464.
   Web of Science ®Google Scholar
• Mudholkar, G. S., and Hutson, A. D. (2000), “The Epsilon–Skew–Normal Distribution for Analyzing Near-Normal Data,” Journal of Statistical Planning and Inference, 83, 291–309. DOI: 10.1016/S0378-3758(99)00096-8.
   Web of Science ®Google Scholar
• Plagborg-Møller, M., Reichlin, L., Ricco, G., and Hasenzagl, T. (2020), “When is Growth at Risk?” Brookings Papers on Economic Activity, 2020, 167–229. DOI: 10.1353/eca.2020.0002.
   Google Scholar
• Ray, P., and Bhattacharya, A. (2018), “Signal Adaptive Variable Selector for the Horseshoe Prior,” arXiv preprint arXiv:1810.09004.
   Google Scholar
• Rossi, B., and Sekhposyan, T. (2019), “Alternative Tests for Correct Specification of Conditional Predictive Densities,” Journal of Econometrics, 208, 638–657. DOI: 10.1016/j.jeconom.2018.07.008.
   Web of Science ®Google Scholar
• Salgado, S., Guvenen, F., and Bloom, N. (2019), “Skewed Business Cycles,” NBER Working Papers 26565.
   Google Scholar
• Segal, G., Shaliastovich, I., and Yaron, A. (2015), “Good and Bad Uncertainty: Macroeconomic and Financial Market Implications,” Journal of Financial Economics, 117, 369–397. DOI: 10.1016/j.jfineco.2015.05.004.
   Web of Science ®Google Scholar
• Smith, A. F. M., and Roberts, G. O. (1993), “Bayesian Computation Via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods,” Journal of the Royal Statistical Society, Series B, 55, 3–23. DOI: 10.1111/j.2517-6161.1993.tb01466.x.
   Google Scholar
• Stock, J. H., and Watson, M. W. (1998), “Median Unbiased Estimation of Coefficient Variance in a Time-Varying Parameter Model,” Journal of the American Statistical Association, 93, 349–358. DOI: 10.1080/01621459.1998.10474116.
   Web of Science ®Google Scholar
• ——— (2002), “Has the Business Cycle Changed and Why?” NBER Macro Annual, 17, 159–218.
   Web of Science ®Google Scholar
• Tapia, R. A. (1977), “Diagonalized Multiplier Methods and Quasi-Newton Methods for Constrained Optimization,” Journal of Optimization Theory and Applications, 22, 135–194. DOI: 10.1007/BF00933161.
   Web of Science ®Google Scholar
• Taylor, J. W. (2019), “Forecasting Value at Risk and Expected Shortfall using a Semiparametric Approach Based on the Asymmetric Laplace Distribution,” Journal of Business & Economic Statistics, 37, 121–133. DOI: 10.1080/07350015.2017.1281815.
   Web of Science ®Google Scholar
• Tian, L., Liu, J. S., and Wei, L. J. (2007), “Implementation of Estimating Function-Based Inference Procedures with Markov Chain Monte Carlo Samplers,” Journal of the American Statistical Association, 102, 881–900. DOI: 10.1198/016214506000000122.
   Web of Science ®Google Scholar
• Vrontos, I. D., Dellaportas, P., and Politis, D. N. (2000), “Full Bayesian Inference for GARCH and EGARCH Models,” Journal of Business & Economic Statistics, 18, 187–198. DOI: 10.2307/1392556.
   Web of Science ®Google Scholar