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Abstract Details

Activity Number: 480
Type: Invited
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract - #303554
Title: Models for High Lead Time Prediction
Author(s): Granville Tunnicliffe-Wilson*+ and John Haywood
Companies: Lancaster University and Victoria University of Wellington
Address: Department of Mathematics and Statistics, Lancaster, LA5 9TB, England
Keywords: Economic cycles ; Whittle likelihood ; Frequency warp
Abstract:

This presentation will describe a new class of linear time series models that have the potential to improve long range prediction for series with power concentrated at lower frequencies. The models will be illustrated by application to the de-seasonalized monthly unemployment figures of the USA. The models are autoregressive in structure but use the generalized shift operator to extend the dependence of the predictor on past values to high lags. Estimation of the model parameters can similarly be weighted to prediction of values at high lead times. The models include just one new discount parameter in addition to the coefficients of a standard autoregression. The presentation will describe how this parameter and the model order can be selected using an information criterion.


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