Ktisis Cyprus University of Technologyhttps://ktisis.cut.ac.cyThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Wed, 29 Jan 2020 21:38:18 GMT2020-01-29T21:38:18Z5081The Annals of Computational and Financial Econometrics, first issuehttps://ktisis.cut.ac.cy/handle/10488/3786Title: The Annals of Computational and Financial Econometrics, first issue
Authors: Zeileis, Achim; Van Dijk, Herman K.; Bauwens, Luc; Belsley, David A.; Koopman, Siem Jan; Mcaleer, Michael; Amendola, Alessandra; Billio, Monica; Croux, Christophe; Chen, Cathy Woan Shu; Davidson, Russell M.; Duchesne, Pierre; Foschi, Paolo; Francq, Christian; Fuertes, Ana Maria; Koop, Gary M.; Khalaf, Lynda; Paolella, Marc S.; Pollock, D. S G; Ruiz, Esther; Paap, Richard; Proietti, Tommaso; Winker, Peter; Yu, Philip; Zakoïan, Jean Michel; Kontoghiorghes, Erricos John
Sun, 01 Jan 2012 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/37862012-01-01T00:00:00ZA comparative study of algorithms for solving seemingly unrelated regressions modelshttps://ktisis.cut.ac.cy/handle/10488/6768Title: A comparative study of algorithms for solving seemingly unrelated regressions models
Authors: Belsley, David A.; Foschi, Paolo; Kontoghiorghes, Erricos John
Abstract: The computational efficiency of various algorithms for solving seemingly unrelated regressions (SUR) models is investigated. Some of the algorithms adapt known methods; others are new. The first transforms the SUR model to an ordinary linear model and uses the QR decomposition to solve it. Three others employ the generalized QR decomposition to solve the SUR model formulated as a generalized linear least-squares problem. Strategies to exploit the structure of the matrices involved are developed. The algorithms are reconsidered for solving the SUR model after it has been transformed to one of smaller dimensions.
Wed, 01 Jan 2003 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/67682003-01-01T00:00:00ZEstimating seemingly unrelated regression models with vector autoregressive disturbanceshttps://ktisis.cut.ac.cy/handle/10488/6770Title: Estimating seemingly unrelated regression models with vector autoregressive disturbances
Authors: Foschi, Paolo; Kontoghiorghes, Erricos John
Abstract: The numerical solution of seemingly unrelated regression (SUR) models with vector autoregressive disturbances is considered. Initially, an orthogonal transformation is applied to reduce the model to one with smaller dimensions. The transformed model is expressed as a reduced-size SUR model with stochastic constraints. The generalized QR decomposition is used as the main computational tool to solve this model. An iterative estimation algorithm is proposed when the variance-covariance matrix of the disturbances and the matrix of autoregressive coefficients are unknown. Strategies to compute the orthogonal factorizations of the non-dense-structured matrices which arise in the estimation procedure are presented. Experimental results demonstrate the computational efficiency of the proposed algorithm.
Wed, 01 Jan 2003 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/67702003-01-01T00:00:00ZEstimation of var models: computational aspectshttps://ktisis.cut.ac.cy/handle/10488/6788Title: Estimation of var models: computational aspects
Authors: Foschi, Paolo; Kontoghiorghes, Erricos John
Abstract: The Vector Autoregressive (VAR) model with zero coefficient restrictions can be formulated as a Seemingly Unrelated Regression Equation (SURE) model. Both the response vectors and the coefficient matrix of the regression equations comprise columns from a Toeplitz matrix. Efficient numerical and computational methods which exploit the Toeplitz and Kronecker product structure of the matrices are proposed. The methods are also adapted to provide numerically stable algorithms for the estimation of VAR(p) models with Granger-caused variables.
Wed, 01 Jan 2003 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/67882003-01-01T00:00:00ZSeemingly unrelated regression model with unequal size observations: computational aspectshttps://ktisis.cut.ac.cy/handle/10488/6790Title: Seemingly unrelated regression model with unequal size observations: computational aspects
Authors: Foschi, Paolo; Kontoghiorghes, Erricos John
Abstract: The computational solution of the seemingly unrelated regression model with unequal size observations is considered. Two algorithms to solve the model when treated as a generalized linear least-squares problem are proposed. The algorithms have as a basic tool the generalized QR decomposition (GQRD) and efficiently exploit the block-sparse structure of the matrices. One of the algorithms reduces the computational burden of the estimation procedure by not computing explicitly the RQ factorization of the GQRD. The maximum likelihood estimation of the model when the covariance matrix is unknown is also considered.
Tue, 01 Jan 2002 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/67902002-01-01T00:00:00ZComputationally efficient methods for solving SURE modelshttps://ktisis.cut.ac.cy/handle/10488/14726Title: Computationally efficient methods for solving SURE models
Authors: Kontoghiorghes, Erricos John; Foschi, Paolo
Abstract: Computationally efficient and numerically stable methods for solving Seemingly Unrelated Regression Equations (SURE) models are proposed. The iterative feasible generalized least squares estimator of SURE models where the regression equations have common exogenous variables is derived. At each iteration an estimator of the SURE model is obtained from the solution of a generalized linear least squares problem. The proposed methods, which have as a basic tool the generalized QR decomposition, are also found to be efficient in the general case where the number of linear independent regressors is smaller than the number of observations.
Mon, 01 Jan 2001 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/147262001-01-01T00:00:00ZAlgorithms for computing the qr decomposition of a set of matrices with common columnshttps://ktisis.cut.ac.cy/handle/10488/6767Title: Algorithms for computing the qr decomposition of a set of matrices with common columns
Authors: Yanev, Petko I.; Foschi, Paolo; Kontoghiorghes, Erricos John
Abstract: The QR decomposition of a set of matrices which have common columns is investigated. The triangular factors of the QR decompositions are represented as nodes of a weighted directed graph. An edge between two nodes exists if and only if the columns of one of the matrices is a subset of the columns of the other. The weight of an edge denotes the computational complexity of deriving the triangular factor of the destination node from that of the source node. The problem is equivalent to constructing the graph and finding the minimum cost for visiting all the nodes. An algorithm which computes the QR decompositions by deriving the minimum spanning tree of the graph is proposed. Theoretical measures of complexity are derived and numerical results from the implementation of this and alternative heuristic algorithms are given.
Thu, 01 Jan 2004 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/67672004-01-01T00:00:00ZA computationally efficient method for solving sur models with orthogonal regressorhttps://ktisis.cut.ac.cy/handle/10488/6774Title: A computationally efficient method for solving sur models with orthogonal regressor
Authors: Foschi, Paolo; Kontoghiorghes, Erricos John
Abstract: A computationally efficient method to estimate seemingly unrelated regression equations models with orthogonal regressors is presented. The method considers the estimation problem as a generalized linear least squares problem (GLLSP). The basic tool for solving the GLLSP is the generalized QR decomposition of the block-diagonal exogenous matrix and Cholesky factor C⊗IT of the covariance matrix of the disturbances. Exploiting the orthogonality property of the regressors the estimation problem is reduced into smaller and independent GLLSPs. The solution of each of the smaller GLLSPs is obtained by a single-column modification of C. This reduces significantly the computational burden of the standard estimation procedure, especially when the iterative feasible estimator of the model is needed. The covariance matrix of the estimators is also derived.
Thu, 01 Jan 2004 00:00:00 GMThttps://ktisis.cut.ac.cy/handle/10488/67742004-01-01T00:00:00Z