Finding starting-values for maximum likelihood estimation of vector STAR models
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.
|Date of creation:||2013|
|Contact details of provider:|| Postal: L 7,1; D - 68161 Mannheim|
Web page: http://www.zew.de/
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Lyra, M. & Paha, J. & Paterlini, S. & Winker, P., 2010.
"Optimization heuristics for determining internal rating grading scales,"
Computational Statistics & Data Analysis,
Elsevier, vol. 54(11), pages 2693-2706, November.
- Marianna Lyra & Johannes Paha & Sandra Paterlini & Peter Winker, 2008. "Optimization Heuristics for Determining Internal Rating Grading Scales," Working Papers 005, COMISEF.
- Marianna Lyra & Johannes Paha & Sandra Paterlini & Peter Winker, 2009. "Optimization Heuristics for Determining Internal Rating Grading Scales," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 09031, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
- Marianna Lyra & Johannes Paha & Sandra Paterlini & Peter Winker, 2008. "Optimization Heuristics for Determining Internal Rating Grading Scales," Center for Economic Research (RECent) 023, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
- Winker, Peter & Fang, Kai-Tai, 1995. "Application of threshold accepting to the evaluation of the discrepancy of a set of points," Discussion Papers, Series II 248, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
- Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
- Terasvirta, Timo & Yang, Yukai, 2014. "Specification, estimation and evaluation of vector smooth transition autoregressive models with applications," CORE Discussion Papers 2014062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Timo Teräsvirta & Yukai Yang, 2014. "Specification, Estimation and Evaluation of Vector Smooth Transition Autoregressive Models with Applications," CREATES Research Papers 2014-08, Department of Economics and Business Economics, Aarhus University.
- Makram El-Shagi, 2011. "An evolutionary algorithm for the estimation of threshold vector error correction models," International Economics and Economic Policy, Springer, vol. 8(4), pages 341-362, December.
- El-Shagi, Makram, 2010. "An Evolutionary Algorithm for the Estimation of Threshold Vector Error Correction Models," IWH Discussion Papers 1/2010, Halle Institute for Economic Research (IWH).
- Yang, Zheng & Tian, Zheng & Yuan, Zixia, 2007. "GSA-based maximum likelihood estimation for threshold vector error correction model," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 109-120, September.
- Francesco Battaglia & Mattheos K. Protopapas, 2011. "Time‐varying multi‐regime models fitting by genetic algorithms," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 237-252, 05.
- Francesco Battaglia & Mattheos Protopapas, 2009. "Time-varying Multi-regime Models Fitting by Genetic Algorithms," Working Papers 009, COMISEF.
- Peter Winker & Dietmar Maringer, 2009. "The convergence of estimators based on heuristics: theory and application to a GARCH model," Computational Statistics, Springer, vol. 24(3), pages 533-550, August.
- Felix Chan & Michael McAleer, 2002. "Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 509-534. Full references (including those not matched with items on IDEAS)