TOOLS FOR NON-LINEAR TIME SERIES FORECASTING IN ECONOMICS - AN EMPIRICAL COMPARISON OF REGIME SWITCHING VECTOR AUTOREGRESSIVE MODELS AND RECURRENT NEURAL NETWORKS

Jane M. Binner, Thomas Elger, Birger Nilsson, Jonathan A. Tepper

    Research output: Chapter in Book/Report/Conference proceedingChapter

    10 Citations (Scopus)

    Abstract

    The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is U.K. inflation and we utilize monthly data from 1969 to 2003. The RS-VAR and the RNN perform approximately on par over both monthly and annual forecast horizons. Both non-linear models perform significantly better than the VAR model.

    Original languageEnglish
    Title of host publicationApplications of Artificial Intelligence in Finance and Economics
    EditorsJane Binner, Graham Kendall, Shu-Heng Chen
    Pages71-91
    Number of pages21
    DOIs
    Publication statusPublished - 1 Dec 2004

    Publication series

    NameAdvances in Econometrics
    Volume19
    ISSN (Print)0731-9053

    ASJC Scopus subject areas

    • Economics and Econometrics

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