A note on interpolation of arima processes

Communications in Statistics - Theory and Methods

Volumn 26, Issue 10, 1997, Pages 2381-2392

  Nieto, Fabio H ^a  

^a UNIVERSIDAD NACIONAL DE COLOMBIA   (Colombia)

Author keywords

ARIMA models; Missing observations; Recursive linear estimation

Indexed keywords

ERROR ANALYSIS; INTERPOLATION; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; MATRIX ALGEBRA; PARAMETER ESTIMATION; PROBLEM SOLVING; VECTORS;

MEAN SQUARE ERROR MATRIX; MISSING DATA VECTOR ESTIMATION; RECURSIVE LINEAR ESTIMATION;

TIME SERIES ANALYSIS;

EID: 0031248815     PISSN: 03610926     EISSN: None     Source Type: Journal    
DOI: 10.1080/03610929708832054     Document Type: Article

References (17)

1. Ansley, C.F. and Kohn, R. (1983). "Exact likelihood of autoregressive moving average process with missing or aggregated data," Biometrika, 70, 275-278.
2. Ansley, C.F. and Kohn, R. (1985). "Estimation, filtering, and smoothing in state space models with incompletely specified initial conditions," The Annals of Statistics. 13, 1286-1316.
3. Bell, W.R. (1984). "Signal extraction for nonstationary time series," The Annals of Statistics, 12, 646-664.
4. Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis: forecasting and control, Holden-Day , Oakland, CA.
5. Brockwell, P.J. and Davis, R.A. (1991). Time Series: Theory and Methods, Springer Verlag, New York.
6. Catlin, D.E. (1989). Estimation, Control, and the Discrete Kalman Filter, Springer Verlag, New York.
7. Chen, C., Liu, L., and Hudak, G.B. (1992). "Modeling and forecasting time series in the presence of outliers and missing data," Working Paper 128, Scientific Computing Associates.
8. Gomez, V., Maravall, A. and Peña, D. (1992). "Computing missing values in time series," Proceedings of the Computational Statistics I, (eds. Y. Dodge and J. Whittaker), Phisica-Verlag-Heilderbug, 283-296.
9. Gomez, V. and Maravall, A. (1994). "Estimation, prediction, and interpo lation for nonstationary series with the Kalman filter," J. Amer. Statist. Assoc. 89(1), 611-624.
10. Guerrero, V.M. (1989). "Optimal conditional ARIMA forecasts," J. of Forecasting, 8, 215-299.
11. Harvey, A.C. and Pierse, R.G. (1984). "Estimating missing observations in economic time series," J. Amer. Statist. Assoc., 79, 125-131.
12. Kohn, R. and Ansley, C.F. (1986). "Estimation, prediction, and interpolation for ARIMA models with missing data," J. Amer. Statist. Assoc., 81, 751-761.
13. Lung, G.M. (1989). "A note on the estimation of missing values in time series," Comm. in Statist., Simul. and Comput., 18, 459-465.
14. Maravall, A. and Peña, D. (1992). "Missing observations and additive outliers in time series models," Working paper 92-40, Statistics and Econometrics series 28, Division de Economia, U. Carlos III de Madrid, Madrid, Spain.
15. Nieto, F.H. (1994). "Una nota sobre la estimacion de datos faltantes en una serie temporal, usando la funcion de autocorrelation dual," Esta distica, 46, 85-103.
16. Nieto, F.H. and Martinez, J. (1996)."A recursive approach for estimating missing observations in an univariate time series," Comm. in Stat., Theory and Meth., 25(9).
17. Peña, D. and Maravall, A. (1991). "Interpolation, outliers, and inverse auto correlations," Comm. Statist. Theory and Meth., 20, 3175-3186.