Vector STAR model , starting-values , optimization heuristics , grid search , estimation , non-linearieties
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.
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