A computationally efficient method for bootstrapping systems of demand equations: A comparison to traditional techniques

Abstract

The solution to a Liapunov matrix equation (LME) has been proposed to estimate the parameters of the demand equations derived from the Translog, the Almost Ideal Demand System and the Rotterdam demand models. When compared to traditional scemingly unrelated regression (SUR) methods the LME approach saves both computer time and space, and it provides parameter estimates that are less likely to suffer from round-off error. However, the LME method is difficult to implement without the use of specially written computer programs and, unlike traditional SUR methods, it does not automatically provide an estimate of the covariance of the parameters. This paper solves these two problems, the first by providing a simplified solution to the Liapunov matrix equation which can be written in a few lines of code in computer languages such as SAS PROC MATRIX/IML^TM or GAUSS^TM; the second, by bootstrapping the parameter covariance matrix.