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Accounting for Missing Values in Score-Driven Time-Varying Parameter Models

Listed author(s):
  • Andre Lucas

    (VU University Amsterdam, the Netherlands)

  • Anne Opschoor

    (VU University Amsterdam, the Netherlands)

  • Julia Schaumburg

    (VU University Amsterdam, the Netherlands)

We show that two alternative perspectives on how to deal with missing data in the context of the score-driven time-varying parameter models of Creal, Koopman, Lucas (2013) and Harvey (2013) lead to precisely the same dynamic transition equations. As score-driven models encompass a wide variety of time-varying parameter models (including generalized autoregressive conditional volatility (GARCH) and duration (ACD) models), the results apply to a wide range of empirically relevant models as applied in economics and statistics.

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File URL: http://papers.tinbergen.nl/16067.pdf
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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 16-067/IV.

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Date of creation: 29 Aug 2016
Handle: RePEc:tin:wpaper:20160067
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  1. F. Blasques & S. J. Koopman & A. Lucas, 2015. "Information-theoretic optimality of observation-driven time series models for continuous responses," Biometrika, Biometrika Trust, vol. 102(2), pages 325-343.
  2. Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andr� Lucas, 2014. "Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 898-915, December.
  3. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
  4. Andrew Harvey & Alessandra Luati, 2014. "Filtering With Heavy Tails," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1112-1122, September.
  5. Engle, Robert F. & Gallo, Giampiero M., 2006. "A multiple indicators model for volatility using intra-daily data," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27.
  6. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
  7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  8. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
  9. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, 08.
  10. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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