(5) (6) (7) where for an industry at time the SCP variables on the left-hand side affect the SCP variables j t on the right-hand side contemporaneously. The system of equations includes a vector of control variables denoted by which are predetermined variables and their values are not determined in Z, the system. Although these variables are not strictly exogenous such as and but R&Dt Growtht following Delorme et al., (2002) we include them in the system of equations because these variables cause the movement of endogenous variables. We also included two diversification variables and because the differences in company scope of operations in terms GeoDivt ProdDivt of geographical coverage, i.e., national, regional, or local differentiate the companies in the industry. Furthermore, the extent of diversification of companies of companies and the path of diversification, i.e., related diversification vs. non-related diversification affect the level of R&D, capital investment, marketing and promotional expenses. We used Seemingly unrelated regression (SUR) approach to estimate the system of linear equations. In the presence of endogenous variables, the ordinary least square (OLS) variables will generate biased and inconsistent estimators. The SUR method involves generalized least square estimation and achieves an improvement in efficiency by taking into account explicit the fact that cross-equation error correlation may not be zero. Although earlier studies have used the Two-stage least square (2SLS) and instrumental variable estimators that yield consistent parameter estimates when equations are simultaneous but each estimation techniques inefficient estimates because these techniques apply only to a single equation within the system of
(5) (6) (7) where for an industry j at time t the SCP variables on the left-hand side affect the SCP variables on the right-hand side contemporaneously. The system of equations includes a vector of control variables denoted by Z, which are predetermined variables and their values are not determined in the system. Although these variables are not strictly exogenous such as R&Dt and Growtht but following Delorme et al., (2002) we include them in the system of equations because these variables cause the movement of endogenous variables. We also included two diversification variables GeoDivt and ProdDivt because the differences in company scope of operations in terms of geographical coverage, i.e., national, regional, or local differentiate the companies in the industry. Furthermore, the extent of diversification of companies of companies and the path of diversification, i.e., related diversification vs. non-related diversification affect the level of R&D, capital investment, marketing and promotional expenses. We used Seemingly unrelated regression (SUR) approach to estimate the system of linear equations. In the presence of endogenous variables, the ordinary least square (OLS) variables will generate biased and inconsistent estimators. The SUR method involves generalized least square estimation and achieves an improvement in efficiency by taking into account explicit the fact that cross-equation error correlation may not be zero. Although earlier studies have used the Two-stage least square (2SLS) and instrumental variable estimators that yield consistent parameter estimates when equations are simultaneous but each estimation techniques inefficient estimates because these techniques apply only to a single equation within the system of
SCP-Final%20Manuscript-31072017.docx