Macroeconometrics and Time Series Analysis pp 269-275 | Cite as
State space models
Chapter
Abstract
State space models is a rather loose term given to time series models, usually formulated in terms of unobserved components, that make use of the state space form for their statistical treatment.
Keywords
State Space Model Stochastic Volatility Signal Extraction State Space Form Dynamic Stochastic General Equilibrium
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Bibliography
- Durbin, J. and Koopman, S. 2001. Time Series Analysis by State Space Methods. Oxford: Oxford University Press.Google Scholar
- Harvey, A. 1989. Forecasting, Structural Time Series Models and Kalman Filter. Cambridge: Cambridge University Press.Google Scholar
- 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
- Harvey, A. and Chung, C.-H. 2000. Estimating the underlying change in unemployment in the UK (with discussion). Journal of the Royal Statistical Society, Series A 163, 303–39.CrossRefGoogle Scholar
- Harvey, A. and de Rossi, G. 2006. Signal extraction. In Palgrave Handbook of Econometrics, vol. 1, ed. K. Patterson and T. Mills. Basingstoke: Palgrave Macmillan.Google Scholar
- Harvey, A., Trimbur, T. and van Dijk, H. 2007. Trends and cycles in economic time series: a Bayesian approach. Journal of Econometrics 140(2), 618–49.CrossRefGoogle Scholar
- Kohn, R., Ansley, C. and Wong, C.-H. 1992. Nonparametric spline regression with autoregressive moving average errors. Biometrika 79, 335–46.CrossRefGoogle Scholar
- Koopman, S. and Harvey, A. 2003. Computing observation weights for signal extraction and filtering. Journal of Economic Dynamics and Control 27, 1317–33.CrossRefGoogle Scholar
- Kuttner, K. 1994. Estimating potential output as a latent variable. Journal of Business and Economic Statistics 12, 361–8.Google Scholar
- Orphanides, A. and van Norden, S. 2002. The unreliability of output gap estimates in real-time. Review of Economics and Statistics 84, 569–83.CrossRefGoogle Scholar
- Pfeffermann, D. 1991. Estimation and seasonal adjustment of population means using data from repeated surveys. Journal of Business and Economic Statistics 9, 163–75.Google Scholar
- Sargent, T. 1989. Two models of measurements and the investment accelerator. Journal of Political Economy 97, 251–87.CrossRefGoogle Scholar
- Smets, F. and Wouter, R. 2003. An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association 1, 1123–75.CrossRefGoogle Scholar
- Shephard, N. 2005. Stochastic Volatility. Oxford: Oxford University Press.Google Scholar
- Whittle, P. 1984. Prediction and Regulation, 2nd edn. Blackwell: Oxford.Google Scholar
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