The SSM Procedure (Experimental)

References

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

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

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

  • Baltagi, B. H. and D. Levin (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 (7).

  • 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(408), 1085–1088.

  • de Jong, P. (1991), The Diffuse Kalman Filter, Annals of Statistics, 19, 1073–83.

  • de Jong, P. and Chu-Chun-Lin, S. (2003), Smoothing with an Unknown Initial Condition, Journal of Time Series Analysis, 24 (2), 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(442), 796–806.

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

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

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

  • 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, Richard 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. and Ansley, C. F. (1991), A Signal Extraction Approach to the Estimation of Treatment and Control Curves, Journal of the American Statistical Association, 86(416), 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 & Economic Statistics, 28(3), 329–343.

  • Nelson, R. and Siegel, A.F. (1987), Parsimonious Modeling of Yield Curves, The Journal of Business, 60(4), 473–489.

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

  • 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(3), 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, 81–9.