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Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility

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Author Info

  • Clark, Todd E.

Abstract

Central banks and other forecasters are increasingly interested in various aspects of density forecasts. However, recent sharp changes in macroeconomic volatility, including the Great Moderation and the more recent sharp rise in volatility associated with increased variation in energy prices and the deep global recession--pose significant challenges to density forecasting. Accordingly, this paper examines, with real-time data, density forecasts of U.S. GDP growth, unemployment, inflation, and the federal funds rate from Bayesian vector autoregression (BVAR) models with stochastic volatility. The results indicate that adding stochastic volatility to BVARs materially improves the real-time accuracy of density forecasts. This article has supplementary material online.

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File URL: http://pubs.amstat.org/doi/abs/10.1198/jbes.2010.09248
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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 29 (2011)
Issue (Month): 3 ()
Pages: 327-341

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Handle: RePEc:bes:jnlbes:v:29:i:3:y:2011:p:327-341

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Cited by:
  1. Barbara Rossi & Tatevik Sehkposyan, 2013. "Evaluating Predictive Densities of U.S. Output Growth and Inflation in a Large Macroeconomic Data Set," Working Papers 689, Barcelona Graduate School of Economics.
  2. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2013. "Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility," Temi di discussione (Economic working papers) 896, Bank of Italy, Economic Research and International Relations Area.
  3. Marco Del Negro & Marc P. Giannoni & Frank Schorfheide, 2014. "Inflation in the Great Recession and New Keynesian Models," NBER Working Papers 20055, National Bureau of Economic Research, Inc.
  4. Wagner Piazza Gaglianone & Jaqueline Terra Moura Marins, 2014. "Risk Assessment of the Brazilian FX Rate," Working Papers Series 344, Central Bank of Brazil, Research Department.
  5. Rossi, Barbara & Sekhposyan, Tatevik, 2014. "Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set," International Journal of Forecasting, Elsevier, vol. 30(3), pages 662-682.
  6. Dave Reifschneider & William Wascher & David Wilcox, 2013. "Aggregate supply in the United States: recent developments and implications for the conduct of monetary policy," Finance and Economics Discussion Series 2013-77, Board of Governors of the Federal Reserve System (U.S.).
  7. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
  8. Andrea CARRIERO & Todd E. CLARK & Massimiliano MARCELLINO, 2012. "Common Drifting Volatility in Large Bayesian VARs," Economics Working Papers ECO2012/08, European University Institute.
  9. Roberto Casarin & Marco Tronzano & Domenico Sartore, 2013. "Bayesian Markov Switching Stochastic Correlation Models," Working Papers 2013:11, Department of Economics, University of Venice "Ca' Foscari".
  10. Clements, Michael P. & Galvão, Ana Beatriz, 2013. "Forecasting with vector autoregressive models of data vintages: US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 29(4), pages 698-714.
  11. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial Conditions and Density Forecasts for US Output and Inflation," Working Papers 715, Queen Mary, University of London, School of Economics and Finance.
  12. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
  13. Luiz Renato Regis de Oliveira Lima & Wagner Piazza Gaglianone, 2012. "Constructing Optimal Density Forecasts from Point Forecast Combinations," Série Textos para Discussão (Working Papers) 5, Programa de Pós-Graduação em Economia - PPGE, Universidade Federal da Paraíba.
  14. Taeyoung Doh & Michael Connolly, 2012. "The state space representation and estimation of a time-varying parameter VAR with stochastic volatility," Research Working Paper RWP 12-04, Federal Reserve Bank of Kansas City.
  15. Manzan, Sebastiano & Zerom, Dawit, 2013. "Are macroeconomic variables useful for forecasting the distribution of U.S. inflation?," International Journal of Forecasting, Elsevier, vol. 29(3), pages 469-478.

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