Finding starting-values for maximum likelihood estimation of vector STAR models

Frauke Schleer

No 13-076, ZEW Discussion Papers from ZEW - Leibniz Centre for European Economic Research

Abstract: This paper focuses on finding starting-values for maximum likelihood estimation of Vector STAR models. Based on a Monte Carlo exercise, different procedures are evaluated. Their performance is assessed w.r.t. model fit and computational effort. I employ i) grid search algorithms, and ii) heuristic optimization procedures, namely, differential evolution, threshold accepting, and simulated annealing. In the equation-by-equation starting-value search approach the procedures achieve equally good results. Unless the errors are cross-correlated, equation-by-equation search followed by a derivative-based algorithm can handle such an optimization problem sufficiently well. This result holds also for higher-dimensional VSTAR models with a slight edge for the heuristic methods. Being faced with more complex Vector STAR models for which a multivariate search approach is required, simulated annealing and differential evolution outperform threshold accepting and the grid with a zoom.

Keywords: Vector STAR model; starting-values; optimization heuristics; grid search; estimation; non-linearieties (search for similar items in EconPapers)
JEL-codes: C32 C61 C63 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:zewdip:13076

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