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State-space models

In: Handbook of Econometrics

Author

Listed:
  • Hamilton, James D.

Abstract

This chapter reviews the usefulness of the Kalman filter for parameter estimation and inference about unobserved variables in linear dynamic systems. Applications include exact maximum likelihood estimation of regressions with ARMA disturbances, time-varying parameters, missing observations, forming an inference about the public's expectations about inflation, and specification of business cycle dynamics. The chapter also reviews models of changes in regime and develops the parallel between such models and linear state-space models. The chapter concludes with a brief discussion of alternative approaches to nonlinear filtering.

Suggested Citation

  • Hamilton, James D., 1986. "State-space models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 50, pages 3039-3080, Elsevier.
    • Handle: RePEc:eee:ecochp:4-50
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    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Nathan S. Balke & Mark E. Wohar, 2002. "Low-Frequency Movements in Stock Prices: A State-Space Decomposition," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 649-667, November.
    2. Randolph & Xiao Qin & Tan Gee Kwang, 2004. "Unit Root Tests with Markov-Switching," Econometric Society 2004 Australasian Meetings 145, Econometric Society.
    3. Gallo, Giampiero M. & Otranto, Edoardo, 2008. "Volatility spillovers, interdependence and comovements: A Markov Switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3011-3026, February.
    4. Mark McClellan & Douglas Staiger, 1999. "The Quality of Health Care Providers," NBER Working Papers 7327, National Bureau of Economic Research, Inc.
    5. Haroon Mumtaz & Nitin Kumar, 2012. "An application of data-rich environment for policy analysis of the Indian economy," Joint Research Papers 2, Centre for Central Banking Studies, Bank of England.
    6. Andrew J. Filardo, 1998. "Choosing information variables for transition probabilities in a time-varying transition probability Markov switching model," Research Working Paper 98-09, Federal Reserve Bank of Kansas City.
    7. Gevorgyan Ruben & Melikyan Narine, 2004. "Missing Data Problem and the Empirical Yield Curve Analysis. An Example of T-bills Market in Armenia," EERC Working Paper Series 04-03e, EERC Research Network, Russia and CIS.
    8. Qin Xiao & Randolph Gee Kwang Tan, 2006. "Markov-switching Unit Root Test: A study of the Property Price Bubbles in Hong Kong and Seoul," Economic Growth Centre Working Paper Series 0602, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    9. Myriam Quispe-Agnoli, 2003. "Stabilization programs and policy credibility: Peru in the 1990s," FRB Atlanta Working Paper 2003-40, Federal Reserve Bank of Atlanta.
    10. Matthias Kredler, 2005. "Bayesian Estimation of a Dynamic Partial-Equilibrium Model for Investment," Econometrics 0509003, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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