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 InfoPaper provided by Halle Institute for Economic Research in its series IWH Discussion Papers with number 1.
Date of creation: Feb 2010
Date of revision:
evolutionary strategy; genetic algorithm; TVECM;
Other versions of this item:
- 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.
- 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; Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-03-06 (All new papers)
- NEP-CMP-2010-03-06 (Computational Economics)
- NEP-ECM-2010-03-06 (Econometrics)
- NEP-ETS-2010-03-06 (Econometric Time Series)
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