Automated variable selection in vector multiplicative error models
AbstractMultiplicative 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 54 (2010)
Issue (Month): 11 (November)
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Web page: http://www.elsevier.com/locate/csda
Other versions of this item:
- Fabrizio Cipollini & Giampiero M. Gallo, 2009. "Automated Variable Selection in Vector Multiplicative Error Models," Econometrics Working Papers Archive wp2009_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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