On generating correlated random variables with a given valid or invalid Correlation matrix

Sudhanshu Mishra (mishrasknehu@yahoo.com)

MPRA Paper from University Library of Munich, Germany

Abstract: In simulation we often have to generate correlated random variables by giving a reference intercorrelation matrix, R or Q. The matrix R is positive definite and a valid correlation matrix. The matrix Q may appear to be a correlation matrix but it may be invalid (negative definite). With R(m,m) it is easy to generate X(n,m), but Q(m,m) cannot give real X(n,m). So, Q has to be converted into the near-most R matrix by some procedure. NJ Higham (2002) provides a method to generate R from Q that satisfies the minimum Frobenius norm condition for (Q-R). Ali Al-Subaihi (2004) gives another method, but his method does not produce an optimal R from Q. In this paper we propose an algorithm to produce an optimal R from Q by minimizing the maximum norm of (Q-R). A Computer program (in FORTRAN) also has been provided. Having obtained R from Q, the paper gives an algorithm to obtain X(n,m) from R(m,m). The proposed algorithm is based on factorization of R, yet it is different from the Kaiser Dichman (1962) procedure. A computer program also has been given.

Keywords: Positive semidefinite; negative definite; maximum norm; frobenius norm; correlated random variables; intercorrelation matrix; correlation matrix; Monte Carlo experiment; multicollinearity; cointegration; computer program; multivariate analysis; simulation; generation of collinear sample data (search for similar items in EconPapers)
JEL-codes: C61 C63 C65 C82 C87 C88 (search for similar items in EconPapers)
Date: 2004-08-02
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