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Time varying VARs with inequality restrictions

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  • Koop, Gary
  • Potter, Simon M.

Abstract

In many applications involving time-varying parameter VARs, it is desirable to restrict the VAR coefficients at each point in time to be non-explosive. This is an example of a problem where inequality restrictions are imposed on states in a state space model. In this paper, we describe how existing MCMC algorithms for imposing such inequality restrictions can work poorly (or not at all) and suggest alternative algorithms which exhibit better performance. Furthermore, we show that previous algorithms involve an approximation relating to a key prior integrating constant. Our algorithms are exact, not involving this approximation. In an application involving a commonly used U.S. data set, we present evidence that the algorithms proposed in this paper work well.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 35 (2011)
Issue (Month): 7 (July)
Pages: 1126-1138

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Handle: RePEc:eee:dyncon:v:35:y:2011:i:7:p:1126-1138

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Web page: http://www.elsevier.com/locate/jedc

Related research

Keywords: Bayesian State space model Markov chain Monte Carlo Metropolis-Hastings;

References

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  1. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1996. "Stochastic Volatility: Likelihood Inference And Comparison With Arch Models," Econometrics, EconWPA 9610002, EconWPA.
  2. Cooley, Thomas F & Prescott, Edward C, 1976. "Estimation in the Presence of Stochastic Parameter Variation," Econometrica, Econometric Society, Econometric Society, vol. 44(1), pages 167-84, January.
  3. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, Elsevier, vol. 52(2), pages 381-419, March.
  4. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 39(s1), pages 3-33, 02.
  5. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2009. "Macroeconomic Forecasting and Structural Change," Working Papers ECARES 2009_020, ULB -- Universite Libre de Bruxelles.
  6. Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2008. "Dynamic probabilities of restrictions in state space models: An application to the Phillips curve," Working Paper Series, The Rimini Centre for Economic Analysis 26-08, The Rimini Centre for Economic Analysis, revised Jan 2008.
  7. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney W., 2009. "On the evolution of the monetary policy transmission mechanism," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 33(4), pages 997-1017, April.
  8. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
  9. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, Biometrika Trust, vol. 89(3), pages 603-616, August.
  10. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
  11. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
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Cited by:
  1. Benjamin Wong, 2013. "Inflation Dynamics and The Role of Oil Shocks: How Different Were the 1970s?," CAMA Working Papers 2013-59, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  2. Chan, Joshua & Koop, Gary & Potter, Simon, 2012. "A new model of trend inflation," MPRA Paper 39496, University Library of Munich, Germany.
  3. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, Elsevier, vol. 30(3), pages 426-448.
  4. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Working Papers 720, Queen Mary, University of London, School of Economics and Finance.
  5. Benjamin Wong, 2013. "The Evolution of the U.S. Output-Inflation Tradeoff," CAMA Working Papers 2013-70, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  6. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," CAMA Working Papers 2014-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  7. Tao Zeng & Yong Li & Jun Yu, 2014. "Deviance Information Criterion for Comparing VAR Models," Working Papers 01-2014, Singapore Management University, School of Economics.

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