Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models

A class of dynamic, nonlinear, statistical models is introduced for the analysis of univariate time series. A distinguishing feature of the models is their reliance on only one primary source of randomness: a sequence of independent and identically distributed normal disturbances. It is established that the models are conditionally Gaussian. This fact is used to define a conditional maximum likelihood method of estimation and prediction. A particular member of the class is shown to provide the statistical foundations for the multiplicative Holt-Winters method of forecasting. This knowledge is exploited to provide methods for computing prediction intervals to accompany the more usual point predictions obtained from the Holt-Winters method. The methods of estimation and prediction are evaluated by simulation. They are also illustrated with an application to Canadian retail sales.

Issue Date:
Apr 01 1995
Publication Type:
Working or Discussion Paper
Total Pages:
Series Statement:
Working Paper No. 4/95

 Record created 2018-02-02, last modified 2018-02-03

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