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Linear errors-in-variables models

  • Manfred Deistler
Conference paper
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 86)

Keywords

Transfer Function High Order Moment Linear Dynamic System Rationality Assumption Schematic Represen 
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References

  1. Aigner, D.J. and A.S. Goldberger (Eds.), (1977): Latent Variables in Socio-Economic Models.North Holland P.C., AmsterdamGoogle Scholar
  2. Aigner, D.J., C. Hsiao, A. Kapteyn and T. Wansbeek (1984): Latent Variable Models in Econometrics. In: Griliches, Z. and M.D. Intriligator (Eds.) Handbook of Econometrics. North Holland P.C., AmsterdamGoogle Scholar
  3. Akaike, H. (1966): On the Use of Non-Gaussian Process in the Identification of a Linear Dynamic System. Annals of the Institute of Statistical Mathematics 18, 269–276Google Scholar
  4. Anderson,B.D.O. (1985): Identification of scalar errors-in-variables models with dynamics, Forthcoming in AutomaticaGoogle Scholar
  5. Anderson, B.D.O. and M. Deistler (1984): Identifiability in Dynamic Errors-in-Variables models, Journal of Time Series Analysis, 5, 1–13Google Scholar
  6. Anderson, T.W. (1984): Estimating Linear Statistical Relationships. Annals of Statistics, 12, 1–45Google Scholar
  7. Brillinger, D.R. (1981): Time Series: Data Analysis and Theory. Expanded Edition. Holden Day, San FranciscoGoogle Scholar
  8. Deistler, M. (1984): Linear errors-in-variables models. In: J. Franke, W. Härdle und D. Martin (Eds.), Robust and Nonlinear Time Series Analysis, Lecture Notes in Statistics, Springer-Verlag, BerlinGoogle Scholar
  9. Deistler,M. (1985a): Linear dynamic errors-in-variables models in: J.Gani and M.Priestley (Eds.) Essays in Time Series and Allied Processes. ForthcomingGoogle Scholar
  10. Deistler,M. (1985b): Identifiability and Causality in Linear Dynamic Errors-in-Variables Systems. In: Proc. 5th Franco Belgian Meeting of Statisticians. ForthcomingGoogle Scholar
  11. Deistler, M. and H.G. Seifert (1978): Identifiability and Consistent Estimability in Dynamic Econometric Models. Econometrica, 46, 969–980Google Scholar
  12. Drion, E.F. (1951): Estimation of the Parameters of a Straight Line and of the Variances of the Variables, if they are Both Subject to Error. Indegationes Math. 13, 256–260Google Scholar
  13. Frisch,R. (1934): Statistical Confluence Analysis by Means of Complete Regression Systems. Publication No. 5, University of Oslo, Economic InstituteGoogle Scholar
  14. Fuller, W.A. (1980): Properties of some Estimators for the Errors-in-Variables Model. Annals of Statistics, 8, 407–422Google Scholar
  15. Geary, R.C. (1942): Inherent Relations between Random Variables. Proceedings of the Royal Irish Academy, Sec. A, 47, 63–76Google Scholar
  16. Geary, R.C. (1943): Relations between Statistics: The General and the Sampling Problem When the Samples are Large. Proceedings of the Royal Irish Academy. Sec. A, 49, 177–196Google Scholar
  17. Gini, C. (1921): Sull'interpolazione di una retta quando i valori della variable indipendente sono affetti da errori accidentali. Metron 1, 63–82Google Scholar
  18. Green,M. and B.D.O.Anderson (1985): Identification of multivariable errors-in-variables models with dynamics. Mimeo.Google Scholar
  19. Hannan, E.J. (1970): Multiple Time Series. Wiley, New YorkGoogle Scholar
  20. Hannan, E.J. and L. Kavalieris (1984): Multivariate Linear Time Series Models. Advances in Applied Probability 16, 492–561Google Scholar
  21. Hinich, M.J. (1983): Estimating the Gain of a Linear Filter from Noisy Data. In: D.R. Brillinger and P.R. Krishnaiah (Eds.) Handbook of Statistics, Vol. 3. North Holland, AmsterdamGoogle Scholar
  22. Hinich,M.J. and W.E.Weber (1984): Estimating Linear Filters with Errors in Variables Using the Hilbert Transform. Federal Reserve Bank of Minneapolis, Res.Dept. Staff Report 96Google Scholar
  23. Kalman, R.E. (1982): System Identification from Noisy Data. In: A. Bednarek and L. Cesari (Eds.) Dynamical Systems II, a University of Florida International Symposium. Academic Press, New YorkGoogle Scholar
  24. Kalman, R.E. (1983): Identifiability and Modeling in Econometrics. In: Krishnaiah, P.R. (Ed.) Developments in Statistics, Vol 4. Academic Press, New YorkGoogle Scholar
  25. Kendall, M.G. and A. Stuart (1969): The Advanced Theory of Statistics. Vol 1, 3rd Edition, Griffin, LondonGoogle Scholar
  26. Klepper, S. and E. Leamer (1984) Consistent Sets of Estimates for Regressions with Errors in all Variables. Econometrica 52, 163–183Google Scholar
  27. Madansky, A. (1959): The Fitting of Straight Lines when Both Variables are Subject to Error. Journal of the American Statistical Association 54, 173–205Google Scholar
  28. Maravall, A. (1979): Identification in Dynamic Shock-Error Models. Springer Verlag, Berlin.Google Scholar
  29. Moran, P.A.P. (1971): Estimating Structural and Functional Relation ships. Journal of Multivariable Analisys 1, 232–255Google Scholar
  30. Nowak, E. (1983): Identification of the Dynamic Shock-Error Model with Autocorrelated Errors. Journal of Econometrics 23, 211–221Google Scholar
  31. Picci, G. (1985): Factor Analylis Models via Stochastic Realization Methods. This VolumeGoogle Scholar
  32. Reiersøl, O. (1941): Confluence Analysis by Means of Lag Moments and other Methods of Confluence Analysis. Econometrica 9, 1–24Google Scholar
  33. Reiersøl, O. (1950): Identifiability of a Linear Relation Between Variables which are subject to Error. Econometrica 18, 375–389Google Scholar
  34. Schneeweiß, H. und H.J. Mittag (1985): Lineare Modelle mit fehlerbehafteten Daten. Physica Verlag, WürzburgGoogle Scholar
  35. Scott, E.L. (1950): Note on Consistent Estimates of the Linear Structural Relation Between two Variables. Annals of Mathematical Statistics 21, 284–288Google Scholar
  36. Söderström, T. (1980): Spectral Decomposition with Application to Identification. In: Archetti, F. and M. Cugiani (Eds.) Numerical Techniques for Stochastic Systems. North Holland P.C., AmsterdamGoogle Scholar
  37. Wegge, L. (1983): ARMAX-Models Parameter Identification without and with Latent Variables. Working Paper. Dept. of Economics, Univ. of California, Davis.Google Scholar

Copyright information

© Springer-Verlag 1986

Authors and Affiliations

  • Manfred Deistler

There are no affiliations available

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