Journal article

Publication year: 2000

Pages: 87-103

Journal: Computational Economics

Volume number: 16

Issue number: 1-2

DOI Link: https://doi.org/10.1023/A:1008757620685

Publisher: Springer

Abstract: 

Model selection – choosing the relevant variables and structures –is a central task in econometrics. Given a limited number of observations,estimation and inference depend on this choice. A frequently treatedmodel-selection problem arises in multivariate autoregressive models, wherethe problem reduces to the choice of a dynamic structure. In most applicationsthis choice is based either on some ad hoc procedure or on a search within avery small subset of all possible models. In this paper the selection isperformed using an explicit optimization approach for a given informationcriterion. Since complete enumeration of all possible lag structures isinfeasible even for moderate dimensions, the global optimization heuristic ofthreshold accepting is implemented. A simulation study compares this approachwith the standard `take all up to the kth lag' approach. It is foundthat, if the lag structure of the true model is sparse, the thresholdaccepting optimization approach gives far better approximations.

Harvard Citation style: Winker, P. (2000) Optimized Multivariate Lag Structure Selection, Computational Economics, 16(1-2), pp. 87-103. https://doi.org/10.1023/A:1008757620685

APA Citation style: Winker, P. (2000). Optimized Multivariate Lag Structure Selection. Computational Economics. 16(1-2), 87-103. https://doi.org/10.1023/A:1008757620685