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Dynamic Factor Analysis with ARMA Factors

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  • Chris Heaton

    (Macquarie University)

  • Victor Solo

    (University of New South Wales)

Abstract

In this paper we present a new approach to the specification of dynamic factor models. Our model has three advantages over existing work. Firstly, it is based on a minimal-dimension state-space representation giving some gain in computational efficiency over existing methods. Secondly, it easily accommodates hypothesis tests about the order of the factor-filter. Thirdly, by allowing the factor-filter to have a common polynomial factor, ARMA-factor models may be estimated with little extra computational expense over the AR- factor case. We illustrate the use of our model with an application to business cycle analysis.

Suggested Citation

  • Chris Heaton & Victor Solo, 2000. "Dynamic Factor Analysis with ARMA Factors," Econometric Society World Congress 2000 Contributed Papers 0145, Econometric Society.
  • Handle: RePEc:ecm:wc2000:0145
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    References listed on IDEAS

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    1. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    2. Geweke, John F & Singleton, Kenneth J, 1981. "Maximum Likelihood "Confirmatory" Factor Analysis of Economic Time Series," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 22(1), pages 37-54, February.
    3. K. J├Âreskog, 1967. "Some contributions to maximum likelihood factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 32(4), pages 443-482, December.
    4. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
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