The SSM Procedure

References

  • Akaike, H. (1974), “A New Look at the Statistical Model Identification,” IEEE Transactions on Automatic Control, AC-19, 716–723.

  • Anderson, B. D. and Moore, J. B. (1979), Optimal Filtering, Englewood Cliffs, NJ: Prentice-Hall.

  • Ansley, C. and de Jong, P. (2012), “Inferring and Predicting Global Temperature Trends,” Slide presentation at "Time Series Econometrics: Conference in Honour of Andrew Harvey.
    URL http://www.oxford-man.ox.ac.uk/sites/default/files/events/Piet%20de%20Jong.pdf

  • Baltagi, B. H. (1995), Econometric Analysis of Panel Data, New York: John Wiley & Sons.

  • Baltagi, B. H. and Levin, D. (1992), “Cigarette Taxation: Raising Revenues and Reducing Consumption,” Structural Change and Economic Dynamics, 3, 321–335.

  • Bell, W. R. (2011), “REGCMPNT—a Fortran Program for Regression Models with ARIMA Component Errors,” Journal of Statistical Software, 41, 1–23.
    URL http://www.jstatsoft.org/v41/i07/

  • Box, G. E. P. and Jenkins, G. M. (1976), Time Series Analysis: Forecasting and Control, Rev. Edition, San Francisco: Holden-Day.

  • Bozdogan, H. (1987), “Model Selection and Akaike’s Information Criterion (AIC): The General Theory and Its Analytical Extensions,” Psychometrika, 52, 345–370.

  • Burnham, K. P. and Anderson, D. R. (1998), Model Selection and Inference: A Practical Information-Theoretic Approach, New York: Springer-Verlag.

  • de Jong, P. (1989), “Smoothing and Interpolation with the State-Space Model,” Journal of the American Statistical Association, 84, 1085–1088.

  • de Jong, P. (1991), “The Diffuse Kalman Filter,” Annals of Statistics, 19, 1073–1083.

  • de Jong, P. and Chu-Chun-Lin, S. (2003), “Smoothing with an Unknown Initial Condition,” Journal of Time Series Analysis, 24, 141–148.

  • de Jong, P. and Mazzi, S. (2001), “Modeling and Smoothing Unequally Spaced Sequence Data,” Statistical Inference for Stochastic Processes, 4, 53–71.

  • de Jong, P. and Penzer, J. (1998), “Diagnosing Shocks in Time Series,” Journal of the American Statistical Association, 93, 796–806.

  • Diggle, P. J., Liang, K.-Y., and Zeger, S. L. (1994), Analysis of Longitudinal Data, Oxford: Clarendon Press.

  • Durbin, J. and Koopman, S. J. (2001), Time Series Analysis by State Space Methods, Oxford: Oxford University Press.

  • Eubank, R. L., Huang, C., and Wang, S. (2003), “Adaptive Order Selection for Spline Smoothing,” Journal of Computational and Graphical Statistics, 12, 382–397.

  • Givens, G. H. and Hoeting, J. A. (2005), Computational Statistics, Hoboken, NJ: John Wiley & Sons.

  • Hannan, E. J. and Quinn, B. G. (1979), “The Determination of the Order of an Autoregression,” Journal of the Royal Statistical Society, Series B, 41, 190–195.

  • Harvey, A. C. (1989), Forecasting, Structural Time Series Models, and the Kalman Filter, Cambridge: Cambridge University Press.

  • Hurvich, C. M. and Tsai, C.-L. (1989), “Regression and Time Series Model Selection in Small Samples,” Biometrika, 76, 297–307.

  • Jones, R. H. (1980), “Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations,” Technometrics, 22, 389–396.

  • Jones, R. H. (1993), Longitudinal Data with Serial Correlation: A State Space Approach, London: Chapman & Hall.

  • Kohn, R., Ansley, C., and Tharm, D. (1991), “The Performance of Cross-Validation and Maximum Likelihood Estimators of Spline Smoothing Parameters,” Journal of the American Statistical Association, 86, 1042–1050.

  • Kohn, R. and Ansley, C. F. (1991), “A Signal Extraction Approach to the Estimation of Treatment and Control Curves,” Journal of the American Statistical Association, 86, 1034–1041.

  • Koopman, S. J., Mallee, M. I. P., and van der Wel, M. (2010), “Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson-Siegel Model with Time-Varying Parameters,” Journal of Business and Economic Statistics, 28, 329–343.

  • Nelson, C. R. and Siegel, A. F. (1987), “Parsimonious Modeling of Yield Curves,” Journal of Business, 60, 473–489.

  • Reinsel, G. C. (1997), Elements of Multivariate Time Series Analysis, 2nd Edition, New York: Springer-Verlag.

  • Schwarz, G. (1978), “Estimating the Dimension of a Model,” Annals of Statistics, 6, 461–464.

  • Selukar, R. S. (2010), “Estimability of the Linear Effects in State Space Models with an Unknown Initial Condition,” Journal of Time Series Analysis, 31, 167–168.

  • Wecker, W. E. and Ansley, C. F. (1983), “The Signal Extraction Approach to Nonlinear Regression and Spline Smoothing,” Journal of the American Statistical Association, 78, 81–89.