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A Note on likelihood estimation of missing values in time series

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  • Peña, Daniel
  • Tiao, George C.

Abstract

Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood, or as random variables and predicted by the expectation of the unknown values given the data. The difference between these two procedures is illustrated by an example. It is argued that the second procedure is, in general, more relevant for estimating missing values in time series.

Suggested Citation

  • Peña, Daniel & Tiao, George C., 1991. "A Note on likelihood estimation of missing values in time series," UC3M Working papers. Economics 2748, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:2748
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    Cited by:

    1. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    2. A. Pérez-González & J. Vilar-Fernández & W. González-Manteiga, 2009. "Asymptotic properties of local polynomial regression with missing data and correlated errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 85-109, March.
    3. Gómez, Víctor & Maravall, Agustín & Peña, Daniel, 1993. "Computing missing values in time series," DES - Working Papers. Statistics and Econometrics. WS 3737, Universidad Carlos III de Madrid. Departamento de Estadística.

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    Keywords

    ARIMA models;

    Statistics

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