A Computational Method for Estimating Continuum Factor Models

Computational Statistics, Vol. 12, No.4 (1997)

Posted: 2 Mar 1998

See all articles by Sara Sjostedt

Sara Sjostedt

University of Umea - Department Of Computing Science

Anders Barrlund

University of Umea - Department Of Computing Science

Abstract

This paper focuses on the computational aspects of a forecasting method for multiple time series, the so called continuum factor models proposed by Sjostedt (1996). Within the vector autoregressive framework, linear transformations of the vector process are considered, revealing possible simplifying structures, as hidden factors catching the important forecasting information. The factors are solutions to nonlinear constrained optimization problems. These optimization problems have a special structure that the solution procedure takes advantage of. The main ideas are to transform the optimization problem to problems with only one constraint, using the Lagrange equations and the Gauss-Newton method. Methods to get initial vectors to the Gauss-Newton method are also proposed. Properties of the computational method are analyzed and discussed. It is also proved that the continuum factors are consistently estimated when sample covariance matrices are used.

JEL Classification: C22, C32, C63

Suggested Citation

Sjostedt-de Luna, Sara and Barrlund, Anders, A Computational Method for Estimating Continuum Factor Models. Computational Statistics, Vol. 12, No.4 (1997), Available at SSRN: https://ssrn.com/abstract=58081

Sara Sjostedt-de Luna (Contact Author)

University of Umea - Department Of Computing Science ( email )

S-90187 Umea
Sweden
+46 (0)90 - 786 51 29 (Phone)

Anders Barrlund

University of Umea - Department Of Computing Science ( email )

S-90187 Umea
Sweden
+46 (0)90 - 786 56 34 (Phone)

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