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Time-series Econometrics: Cointegration and Autoregressive Conditional Heteroskedasticity

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    Abstract

    Advanced information on the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel, 2003. Empirical research in macroeconomics as well as in financial economics is largely based on time series. Ever since Economics Laureate Trygve Haavelmo's work it has been standard to view economic time series as realizations of stochastic processes. This approach allows the model builder to use statistical inference in constructing and testing equations that characterize relationships between economic variables. This year's Prize rewards two contributions that have deepened our understanding of two central properties of many economic time series - nonstationarity and time-varying volatility - and have led to a large number of applications

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    File URL: http://www.nobelprize.org/nobel_prizes/economics/laureates/2003/advanced-economicsciences2003.pdf
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    Bibliographic Info

    Paper provided by Nobel Prize Committee in its series Nobel Prize in Economics documents with number 2003-1.

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    Length: 31 pages
    Date of creation: 08 Oct 2003
    Date of revision:
    Handle: RePEc:ris:nobelp:2003_001

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    Web page: http://www.nobelprize.org

    Related research

    Keywords: time-series; cointegration;

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    References

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    Cited by:
    1. Geetesh Bhardwaj & Norman Swanson, 2004. "An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series," Departmental Working Papers 200422, Rutgers University, Department of Economics.

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