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Journal of Applied Mathematics and Decision Sciences
Volume 2006, Article ID 12619, 12 pages
http://dx.doi.org/10.1155/JAMDS/2006/12619

A structural model with interventions for new Zealand sawn timber production

1Fonterra Research Centre, Palmerston North 5301, New Zealand
2Institute of Information Sciences and Technology, Massey University, Palmerston North 5301, New Zealand
3Innovation and Research, Ministry of Agriculture Forestry, Wellington 4000, New Zealand

Received 16 January 2005; Revised 16 May 2005; Accepted 17 May 2005

Copyright © 2006 Dongwen Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. Akaike, “A new look at the statistical model identification,” IEEE Transactions on Automatic Control, vol. 19, no. 6, pp. 716–723, 1974. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  2. C. L. Brown and J. Valentine, “The process and implications of privatization for forestry institutions: focus on New Zealand,” Unasylva, vol. 45, no. 178, pp. 11–19, 1994. View at Google Scholar
  3. C. K. Carter and R. Kohn, “On Gibbs sampling for state space models,” Biometrika, vol. 81, no. 3, pp. 541–553, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  4. M. Clarke, “Devolving forest ownership through privatization in New Zealand,” Unasylva, vol. 50, no. 199, pp. 35–44, 1999. View at Google Scholar
  5. P. de Jong, “Smoothing and interpolation with the state-space model,” Journal of the American Statistical Association, vol. 84, no. 408, pp. 1085–1088, 1989. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  6. P. de Jong and J. Penzer, “Diagnosing shocks in time series,” Journal of the American Statistical Association, vol. 93, no. 442, pp. 796–806, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  7. L. P. Hansen and J. J. Heckman, “The empirical foundations of calibration,” Journal of Economic Perspectives, vol. 10, no. 1, pp. 87–104, 1996. View at Google Scholar
  8. A. C. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press, Cambridge, 1989.
  9. A. C. Harvey, Time Series Models, Harvester Wheatsheaf, New York, 2nd edition, 1993. View at Zentralblatt MATH
  10. A. C. Harvey and J. Durbin, “The effects of seat belt legislation on British road casualties: a case study in structural time series modelling,” Journal of the Royal Statistical Society. Series A, vol. 149, pp. 187–227, 1986. View at Publisher · View at Google Scholar
  11. A. C. Harvey and A. Jaeger, “Detrending, stylized facts and the business cycle,” Journal of Applied Econometrics, vol. 8, pp. 231–247, 1993. View at Google Scholar
  12. R. Hodrick and E. Prescott, “Post-war US Business cycles: an empirical investigation,” Journal of Money, Credit and Banking, vol. 29, no. 1, pp. 1–16, 1997. View at Publisher · View at Google Scholar
  13. R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME - Journal of Basic Engineering, vol. 8, pp. 95–108, 1960. View at Google Scholar
  14. D. Luo, “Intervention analysis in the basic structural model,” unpublished Master of Applied Statistics thesis, Massey University, Palmerston North, New Zealand, 1999.
  15. A. Pole, M. West, and J. Harrison, Applied Bayesain Forecasting and Time Series Analysis, Chapman & Hall, New York, 1994. View at Zentralblatt MATH
  16. D. Quah, “The relative importance of permanent and transitory components: identification and some theoretical bounds,” Econometrica, vol. 60, pp. 107–118, 1992. View at Publisher · View at Google Scholar
  17. D. Rhodes and J. Novis, “The Impact of Incentives on the Development of Plantation Forest Resources in New Zealand,” MAF Information Paper No: 45, 2002.
  18. F. C. Schweppe, “Evaluation of likelihood functions for Gaussian signals,” IEEE Transactions on Information Theory, vol. 11, no. 1, pp. 61–70, 1965. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  19. J. Shiskin, A. H. Young, and J. C. Musgrave, “The X-11 Variant of the Census Method II Seasonal Adjustment Program,” Technical Paper 15, US Deptartment of Commerce, Bureau of the Census, Washington, DC, 1967. View at Google Scholar
  20. R. H. Shumway and D. S. Stoffer, “An approach to time series smoothing and forecasting using the EM algorithm,” Journal of Time Series Analysis, vol. 3, pp. 253–264, 1982. View at Google Scholar
  21. R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its Applications, Springer Texts in Statistics, Springer, New York, 2000. View at Zentralblatt MATH · View at MathSciNet
  22. M. W. Watson, “Univariate detrending methods with stochastic trends,” Journal of Monetary Economics, vol. 18, no. 1, pp. 49–75, 1986. View at Publisher · View at Google Scholar