The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

State Space Models

  • Andrew Harvey
Reference work entry


The state space form opens the way to the statistical treatment of a wide range of dynamic models in a unified framework. For models formulated in unobserved components it offers algorithms for filtering, signal extraction and prediction. Data irregularities can be handled and recent work on computational methods has extended the range of nonlinear and non-Gaussian models that can be adopted for practical use.


ARIMA models Dynamic stochastic general equilibrium (DSGE) models Finite sample computation Hodrick–Prescott filter International Labor Organization (ILO) Kalman filter Linear rational expectations model Markov chain Monte Carlo Maximum likelihood Mean square errors Missing observations Nowcasting Output gap Particle filtering Phillips curve Prediction Smoothing State space form State space models State vector Stochastic volatility models Structural time series models Wiener–Kolomogorov (WK) filter 

JEL Classifications

D4 D10 C22 C23 C51 
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  1. Durbin, J., and S. Koopman. 2001. Time series analysis by state space methods. Oxford: Oxford University Press.Google Scholar
  2. Harvey, A. 1989. Forecasting, structural time series models and Kalman filter. Cambridge: Cambridge University Press.Google Scholar
  3. Harvey, A. 2006. Forecasting with unobserved components time series models. In Handbook of economic forecasting, vol. 1, ed. G. Elliot, C. Granger, and A. Timmermann. Amsterdam: North-Holland.Google Scholar
  4. Harvey, A., and C.-H. Chung. 2000. Estimating the underlying change in unemployment in the UK (with discussion). Journal of the Royal Statistical Society, Series A 163: 303–339.CrossRefGoogle Scholar
  5. Harvey, A., and G. de Rossi. 2006. Signal extraction. In Palgrave handbook of econometrics, vol. 1, ed. K. Patterson and T. Mills. Basingstoke: Palgrave Macmillan.Google Scholar
  6. Harvey, A., T. Trimbur, and H. van Dijk. 2007. Trends and cycles in economic time series: A Bayesian approach. Journal of Econometrics 140(2): 618–649.CrossRefGoogle Scholar
  7. Kohn, R., C. Ansley, and C.-H. Wong. 1992. Nonparametric spline regression with autoregressive moving average errors. Biometrika 79: 335–346.CrossRefGoogle Scholar
  8. Koopman, S., and A. Harvey. 2003. Computing observation weights for signal extraction and filtering. Journal of Economic Dynamics and Control 27: 1317–1333.CrossRefGoogle Scholar
  9. Kuttner, K. 1994. Estimating potential output as a latent variable. Journal of Business and Economic Statistics 12: 361–368.Google Scholar
  10. Orphanides, A., and S. van Norden. 2002. The unreliability of output gap estimates in real-time. Review of Economics and Statistics 84: 569–583.CrossRefGoogle Scholar
  11. Pfeffermann, D. 1991. Estimation and seasonal adjustment of population means using data from repeated surveys. Journal of Business and Economic Statistics 9: 163–175.Google Scholar
  12. Sargent, T. 1989. Two models of measurements and the investment accelerator. Journal of Political Economy 97: 251–287.CrossRefGoogle Scholar
  13. Smets, F., and R. Wouter. 2003. An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association 1: 1123–1175.CrossRefGoogle Scholar
  14. Shephard, N. 2005. Stochastic volatility. Oxford: Oxford University Press.Google Scholar
  15. Whittle, P. 1984. Prediction and regulation, 2nd ed. Blackwell: Oxford.Google Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Andrew Harvey
    • 1
  1. 1.