Optimization of the Generalized Covariance Estimator in Noncausal Processes

38 Pages Posted: 25 Apr 2024

See all articles by Gianluca Cubadda

Gianluca Cubadda

University of Rome Tor Vergata - Department of Economics and Finance

Francesco Giancaterini

University of Rome Tor Vergata

Alain Hecq

Maastricht University - Department of Quantitative Economics

Joann Jasiak

York University - Department of Economics

Date Written: April 23, 2024

Abstract

This paper investigates the performance of routinely used optimization algorithms in application to the Generalized Covariance estimator (GCov) for univariate and multivariate mixed causal and noncausal models. The GCov is a semi-parametric estimator with an objective function based on nonlinear autocovariances to identify causal and noncausal orders. When the number and type of nonlinear autocovariances included in the objective function are insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise. These issues result in local minima in the objective function, which correspond to parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point and the optimization algorithm employed, the algorithm can converge to a local minimum. The paper proposes the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that SA performs well in its application to mixed causal and noncausal models, successfully eliminating the effects of local minima. The proposed approach is illustrated by an empirical study of a bivariate series of commodity prices.

Keywords: Mixed causal and noncausal models, Generalized covariance estimator, Simulated Annealing, Optimization, Commodity prices

Suggested Citation

Cubadda, Gianluca and Giancaterini, Francesco and Hecq, Alain and Jasiak, Joann, Optimization of the Generalized Covariance Estimator in Noncausal Processes (April 23, 2024). CEIS Working Paper No. 574, Available at SSRN: https://ssrn.com/abstract=4804375 or http://dx.doi.org/10.2139/ssrn.4804375

Gianluca Cubadda (Contact Author)

University of Rome Tor Vergata - Department of Economics and Finance ( email )

Via Columbia n.2
Roma, 00133
Italy

Francesco Giancaterini

University of Rome Tor Vergata ( email )

Roma
Italy

Alain Hecq

Maastricht University - Department of Quantitative Economics ( email )

P.O. Box 616
Maastricht, 6200 MD
Netherlands

HOME PAGE: http://www.maastrichtuniversity.nl/a.hecq

Joann Jasiak

York University - Department of Economics ( email )

4700 Keele St.
Toronto, Ontario M3J 1P3
Canada
416-736-2100 EX77045 (Phone)
416-736-5987 (Fax)

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