Journalartikel

Jahr der Veröffentlichung: 2000

Seiten: 87-103

Zeitschrift: Computational Economics

Bandnummer: 16

Heftnummer: 1-2

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

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

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