An evolutionary algorithm for the estimation of threshold vector error correction models
AbstractWe develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since disregarding a threshold in cointegration models renders standard approaches to the estimation of the cointegration vectors inefficient, TVECM necessitate a simultaneous estimation of the cointegration vector(s) and the threshold. As far as two cointegrated variables are considered this is commonly achieved by a grid search. However, grid search quickly becomes computationally unfeasible if more than two variables are cointegrated. Therefore, the likelihood function has to be maximized using heuristic approaches. Depending on the precise problem structure the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.
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Bibliographic InfoArticle provided by Springer in its journal International Economics and Economic Policy.
Volume (Year): 8 (2011)
Issue (Month): 4 (December)
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Web page: http://www.springerlink.com/link.asp?id=111059
Threshold cointegration; Evolutionary algorithms; Genetic algorithms; Evolutionary strategies; C61; C32;
Other versions of this item:
- Makram El-Shagi, 2010. "An Evolutionary Algorithm for the Estimation of Threshold Vector Error Correction Models," IWH Discussion Papers 1, Halle Institute for Economic Research.
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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