Dynamic Factor Analysis with ARMA Factors
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.
|Date of creation:||01 Aug 2000|
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- Chamberlain, Gary & Rothschild, Michael, 1983.
"Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets,"
Econometric Society, vol. 51(5), pages 1281-304, September.
- Chamberlain, Gary & Rothschild, Michael, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Scholarly Articles 3230355, Harvard University Department of Economics.
- Gary Chamberlain & Michael Rothschild, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," NBER Working Papers 0996, National Bureau of Economic Research, Inc.
- 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.
- 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.
- K. Jöreskog, 1967. "Some contributions to maximum likelihood factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 32(4), pages 443-482, December.
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