Paper #213

Título:

Forecasting with missing data: Application to a real case

Autores:

Pedro Delicado y Ana Justel

Data:

Mayo 1997

Resumen:

This paper presents a comparative analysis of linear and mixed models for short term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay. The series is interpolated with a linear predictor which minimizes the forecast mean square error. The linear models are seasonal ARIMA models and the mixed models have a linear component and a non linear seasonal component. The non linear component is estimated by a non parametric regression of data versus time. Short term forecasts, no more than two days ahead, are of interest because they can be used by the port authorities to notice the fleet. Several models are fitted and compared by their forecasting behavior.

Palabras clave:

Significant wave height, mean square error, linear interpolation, ARIMA models, nonparametric smoothing

Códigos JEL:

C22, C14

Área de investigación:

Estadística, Econometría y Métodos Cuantitativos

Publicado en:

Journal of Forecasting, 18, 285-298, 1999
Con el título:
Forecasting with missing data: Application to coastal wave heights

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