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Financial conditions and density forecasts for US Output and inflation

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  • Piergiorgio Alessandri
  • Haroon Mumtaz

Abstract

The authors reassess the predictive power of financial indicators for output and inflation in the US by studying predictive densities generated by set of linear and nonlinear forecasting models. They argue that, if the linkage between financial and real economy is state-dependent as implied by standard models with financial frictions, predictive densities should reveal aspects of the co-movements between financial and macroeconomic variables that are ignored by construction in an ordinary (central) forecasting exercise. The authors study the performance of linear and nonlinear (Threshold and Markov-Switching) VARs estimated on a monthly US dataset including various commonly-used financial indicators. We obtain three important results. First, adding financial indicators to an otherwise standard VAR improves both central forecasts and predictive distributions for output, but the improvement is more substantial for the latter. Even in a linear model, financial indicators are more useful in predicting 'tails', or deviations of output and inflation from their expected paths, than 'means', namely the expected paths themselves. Second, nonlinear models with financial indicators tend to generate noisier central forecasts than their linear counterparts, but they clearly outperform them in predicting distributions. This is mainly because nonlinear models predict the likelihood of recessionary episodes more accurately. Third, the discrepancies between models are themselves predictable: a Bayesian forecaster can formulate a reasonable real-time guess on which model is likely to be more accurate in the near future.Â

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Bibliographic Info

Paper provided by Centre for Central Banking Studies, Bank of England in its series Joint Research Papers with number 4.

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Date of creation: May 2013
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Handle: RePEc:ccb:jrpapr:4

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Keywords: Financial conditions; density forecasts; US; output; inflation;

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  1. Cogley, Timothy & Morozov, Sergei & Sargent, Thomas J., 2005. "Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1893-1925, November.
  2. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
  3. Angus Deaton, 1989. "Saving and Liquidity Constraints," NBER Working Papers 3196, National Bureau of Economic Research, Inc.
  4. Gertler, Mark & Karadi, Peter, 2011. "A model of unconventional monetary policy," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 17-34, January.
  5. Clark, Todd E., 2011. "Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 327-341.
  6. Raffaella Giacomini & Halbert White, 2003. "Tests of conditional predictive ability," Boston College Working Papers in Economics 572, Boston College Department of Economics.
  7. Nolan, Charles & Thoenissen, Christoph, 2009. "Financial shocks and the US business cycle," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 596-604, May.
  8. Bańbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Large Bayesian VARs," Working Paper Series 0966, European Central Bank.
  9. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," NBER Working Papers 19469, National Bureau of Economic Research, Inc.
  10. Den Haan, Wouter J. & De Wind, Joris, 2012. "Nonlinear and stable perturbation-based approximations," Journal of Economic Dynamics and Control, Elsevier, vol. 36(10), pages 1477-1497.
  11. Javier Bianchi, 2009. "Overborrowing and systemic externalities in the business cycle," Working Paper 2009-24, Federal Reserve Bank of Atlanta.
  12. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
  13. Vladimir Yankov & Egon Zakrajsek & Simon Gilchrist, 2009. "Credit Market Shocks and Economic Fluctuations: Evidence from Corporate Bond and Stock Markets," 2009 Meeting Papers 514, Society for Economic Dynamics.
  14. Nathan S. Balke, 2000. "Credit and Economic Activity: Credit Regimes and Nonlinear Propagation of Shocks," The Review of Economics and Statistics, MIT Press, vol. 82(2), pages 344-349, May.
  15. Sydney Ludvigson, 1996. "Consumption and credit: a model of time-varying liquidity constraints," Research Paper 9624, Federal Reserve Bank of New York.
  16. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
  17. Gianni Amisano & Raffaella Giacomini, 2005. "Comparing Density Forecsts via Weighted Likelihood Ratio Tests," Working Papers ubs0504, University of Brescia, Department of Economics.
  18. Zheng Liu & Pengfei Wang & Tao Zha, 2013. "Land‐Price Dynamics and Macroeconomic Fluctuations," Econometrica, Econometric Society, vol. 81(3), pages 1147-1184, 05.
  19. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  20. McCallum, John, 1991. "Credit Rationing and the Monetary Transmission Mechanism," American Economic Review, American Economic Association, vol. 81(4), pages 946-51, September.
  21. Filardo, Andrew J. & Gordon, Stephen F., 1998. "Business cycle durations," Journal of Econometrics, Elsevier, vol. 85(1), pages 99-123, July.
  22. Kim, Sunghyun Henry & Kollmann, Robert & Kim, Jinill, 2010. "Solving the incomplete market model with aggregate uncertainty using a perturbation method," Journal of Economic Dynamics and Control, Elsevier, vol. 34(1), pages 50-58, January.
  23. Li, Shuyun May & Dressler, Scott, 2011. "Business cycle asymmetry via occasionally binding international borrowing constraints," Journal of Macroeconomics, Elsevier, vol. 33(1), pages 33-41, March.
  24. James Mitchell & Kenneth F. Wallis, 2011. "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 1023-1040, 09.
  25. Gertler, Mark & Kiyotaki, Nobuhiro, 2010. "Financial Intermediation and Credit Policy in Business Cycle Analysis," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 11, pages 547-599 Elsevier.
  26. Scott Brave & R. Andrew Butters, 2012. "Diagnosing the Financial System: Financial Conditions and Financial Stress," International Journal of Central Banking, International Journal of Central Banking, vol. 8(2), pages 191-239, June.
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