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A joint Bayesian forecasting model of judgment and observed data:Working paper 2012: 4

Davydenko, Andrey and Fildes, Robert (2012) A joint Bayesian forecasting model of judgment and observed data:Working paper 2012: 4. Working Paper. Department of Management Science, Lancaster University, Lancaster.

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    Abstract

    This paper presents a new approach that aims to incorporate prior judgmental forecasts into a statistical forecasting model. The result is a set of forecasts that are consistent with both the judgment and latest observations. The approach is based on constructing a model with a combined dataset where the expert forecasts and the historical data are described by means of corresponding regression equations. Model estimation is done using numeric Bayesian analysis. Semiparametric methods are used to ensure finding adequate forecasts without any prior knowledge of the specific type of the trend function. The expert forecasts can be provided as estimates of future time series values or as estimates of total or average values over any particular time intervals. Empirical analysis has shown that the approach is operable in practical settings. Compared to standard methods of combining, the approach is more flexible and in empirical comparisons proves to be more accurate.

    Item Type: Monograph (Working Paper)
    Uncontrolled Keywords: Forecasting accuracy ; combining statistical methods and judgement
    Subjects: H Social Sciences > HB Economic Theory
    Departments: Lancaster University Management School > Management Science
    ID Code: 60227
    Deposited By: ep_importer_pure
    Deposited On: 22 Nov 2012 15:36
    Refereed?: No
    Published?: Published
    Last Modified: 13 Mar 2013 16:55
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/60227

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