Automated variable selection in vector multiplicative error models

Fabrizio Cipollini () and Giampiero Gallo ()

Computational Statistics & Data Analysis, 2010, vol. 54, issue 11, 2470-2486

Abstract: Multiplicative Error Models (MEM) can be used to trace the dynamics of non-negative valued processes. Interactions between several such processes are accommodated by the vector MEM (vMEM) in the form of parametric (estimated by Maximum Likelihood) or semiparametric specifications (estimated by Generalized Method of Moments). In choosing the relevant variables an automated procedure can be followed where the full specification is successively pruned in a general-to-specific approach. An efficient and fast algorithm is presented and evaluated by means of simulations. The empirical application shows the interdependence across European markets and the relative strength of volatility spillovers.

Date: 2010
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Working Paper: Automated Variable Selection in Vector Multiplicative Error Models (2009)
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