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Identification Of Unobserved Components Models

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  • Luiz Koodi Hotta

Abstract

. Unobserved components ARIMA models are common in time series applications. However, fitting models of this type leads to problems of model identification. In this paper we derive a methodology to check whether a proposed model is identifiable. We show that this kind of identification can be checked using the autocovariance generating function and/or the (pseudo‐)spectral generating function.

Suggested Citation

  • Luiz Koodi Hotta, 1989. "Identification Of Unobserved Components Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 259-270, May.
    • Handle: RePEc:bla:jtsera:v:10:y:1989:i:3:p:259-270
    DOI: 10.1111/j.1467-9892.1989.tb00027.x
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    Cited by:

    1. Dante Amengual & Xinyue Bei & Enrique Sentana, 2023. "Highly Irregular Serial Correlation Tests," Working Papers wp2023_2302, CEMFI.
    2. Trenkler, Carsten & Weber, Enzo, 2016. "On the identification of multivariate correlated unobserved components models," Economics Letters, Elsevier, vol. 138(C), pages 15-18.
    3. Gabriele Fiorentini & Enrique Sentana, 2016. "Neglected serial correlation tests in UCARIMA models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 121-178, March.
    4. Li, Mengheng & Mendieta-Muñoz, Ivan, 2024. "Dynamic hysteresis effects," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    5. Zirogiannis, Nikolaos & Tripodis, Yorghos, 2013. "A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm," Working Paper Series 142752, University of Massachusetts, Amherst, Department of Resource Economics.
    6. Antonis Demos, 2002. "Moments and dynamic structure of a time-varying parameter stochastic volatility in mean model," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 345-357, June.
    7. Nikolaos Zirogiannis & Yorghos Tripodis, 2018. "Dynamic factor analysis for short panels: estimating performance trajectories for water utilities," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 131-150, March.
    8. Wolff, Christian C. P., 2000. "Measuring the forward foreign exchange risk premium: multi-country evidence from unobserved components models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 10(1), pages 1-8, January.
    9. Ghysels, Eric & Granger, Clive W J & Siklos, Pierre L, 1996. "Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 374-386, July.
    10. Zirogiannis, Nikolaos & Tripodis, Yorghos, 2014. "Dynamic Factor Analysis for Short Panels: Estimating Performance Trajectories for Water Utilities," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170592, Agricultural and Applied Economics Association.
    11. Nikolaos Zirogiannis & Yorghos Tripodis, 2013. "A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm," Working Papers 2013-1, University of Massachusetts Amherst, Department of Resource Economics.

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