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A flexible approach to parametric inference in nonlinear and time varying time series models

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Author Info

  • Gary Koop

    ()
    (Department of Economics - University of Strathclyde)

  • Simon Potter

    ()

Abstract

Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible modeling approach which can accommodate virtually any of these specifications. We build on earlier work showing the relationship between flexible functional forms and random variation in parameters. Our contribution is based around the use of priors on the time variation that is developed from considering a hypothetical reordering of the data and distance between neighboring (reordered) observations. The range of priors produced in this way can accommodate a wide variety of nonlinear time series models, including those with regime-switching and structural breaks. By allowing the amount of random variation in parameters to depend on the distance between (reordered) observations, the parameters can evolve in a wide variety of ways, allowing for everything from models exhibiting abrupt change (e.g. threshold autoregressive models or standard structural break models) to those which allow for a gradual evolution of parameters (e.g. smooth transition autoregressive models or time varying parameter models). Bayesian econometric methods for inference are developed for estimating the distance function and types of hypothetical reordering. Conditional on a hypothetical reordering and distance function, a simple reordering of the actual data allows us to estimate our models with standard state space methods by a simple adjustment to the measurement equation. We use artificial data to show the advantages of our approach, before providing two empirical illustrations involving the modeling of real GDP growth.

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Bibliographic Info

Paper provided by HAL in its series Post-Print with number peer-00732535.

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Date of creation: 15 Sep 2010
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Publication status: Published, Journal of Econometrics, 2010, 159, 1, 134
Handle: RePEc:hal:journl:peer-00732535

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Related research

Keywords: C11; C22; E17; Bayesian; Structural break; Threshold autoregressive; Regime switching; State space model;

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  1. Chang-Jin Kim & Charles Nelson & Jeremy M. Piger, 2003. "The less volatile U.S. economy: a Bayesian investigation of timing, breadth, and potential explanations," Working Papers, Federal Reserve Bank of St. Louis 2001-016, Federal Reserve Bank of St. Louis.
  2. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 10(2), pages 109-25, April-Jun.
  3. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 26, pages 66-77, January.
  4. Gary Koop & Dale J. Poirer, 2004. "Bayesian Variants of Some classical Semiparametric Regression Techniques," ESE Discussion Papers, Edinburgh School of Economics, University of Edinburgh 73, Edinburgh School of Economics, University of Edinburgh.
  5. Koop, Gary & Potter, Simon M, 1999. "Dynamic Asymmetries in U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 17(3), pages 298-312, July.
  6. Boivin, Jean & Giannoni, Marc, 2006. "Has Monetary Policy Become More Effective?," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5463, C.E.P.R. Discussion Papers.
  7. Margaret McConnell & Gabriel Perez Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, Federal Reserve Bank of San Francisco, issue Mar.
  8. 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.
  9. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 763-789.
  10. Sangjoon Kim, Neil Shephard & Siddhartha Chib, . "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
  11. Lundbergh, Stefan & Teräsvirta, Timo & van Dijk, Dick, 2000. "Time-Varying Smooth Transition Autoregressive Models," Working Paper Series in Economics and Finance 376, Stockholm School of Economics.
  12. Beaudry, Paul & Koop, Gary, 1993. "Do recessions permanently change output?," Journal of Monetary Economics, Elsevier, Elsevier, vol. 31(2), pages 149-163, April.
  13. 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.
  14. Gary Koop & Simon M. Potter, 2001. "Are apparent findings of nonlinearity due to structural instability in economic time series?," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 4(1), pages 38.
  15. Frédérique Bec & Anders Rahbek & Neil Shephard, 2008. "The ACR model: a multivariate dynamic mixture autoregression," THEMA Working Papers 2008-11, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  16. Neil Shephard & Anders Rahbek, 2002. "Autoregressive conditional root model," Economics Series Working Papers 2002-W07, University of Oxford, Department of Economics.
  17. Olivier Blanchard & John Simon, 2001. "The Long and Large Decline in U.S. Output Volatility," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 32(1), pages 135-174.
  18. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques 9552, Universite de Montreal, Departement de sciences economiques.
  19. 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.
  20. 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.
  21. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, Elsevier, vol. 86(2), pages 221-241, June.
  22. Hamilton, James D, 2001. "A Parametric Approach to Flexible Nonlinear Inference," Econometrica, Econometric Society, Econometric Society, vol. 69(3), pages 537-73, May.
  23. Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 3(1), pages 84-107.
  24. 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.
  25. James D. Hamilton, 2000. "What is an Oil Shock?," NBER Working Papers 7755, National Bureau of Economic Research, Inc.
  26. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, Elsevier, vol. 75(1), pages 79-97, November.
  27. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, Elsevier, vol. 79(1), pages 169-193, July.
  28. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198523543, October.
  29. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
  30. Adonis Yatchew, 1998. "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, American Economic Association, vol. 36(2), pages 669-721, June.
  31. Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, Elsevier, vol. 137(1), pages 112-133, March.
  32. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, Econometric Society, vol. 57(2), pages 357-84, March.
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Cited by:
  1. Claudio Morana, 2013. "The oil price-macroeconomy relationship since the mid-1980s: A global perspective," Working Papers, University of Milano-Bicocca, Department of Economics 223, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
  2. Aue, Alexander & Horváth, Lajos & Hušková, Marie, 2012. "Segmenting mean-nonstationary time series via trending regressions," Journal of Econometrics, Elsevier, Elsevier, vol. 168(2), pages 367-381.

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