Hybrid Methodology for Sparse Selection of Generalized Estimating Equations Model for the Drivers of Firm Value

The study proposes a two-step hybrid methodology for sparse generalized estimation equations modeling of the drivers of shareholder value creation. Through the methodology, the validity of the Gordon constant growth model is established and other non -dividend factors' contribution to shareholder value creation is assessed. The two-step hybrid method involves picking out the right intra-subject correlation matrix and set of regressors using EAIC and QIC respectively (EAIC-QIC) and then obtaining the penalized GEE estimators of the selected model. Penalization is useful in removing redundant regressors from the final model. The performance of the proposed method was compared to that of exclusively using QIC method in selecting both the correlation matrix and set of regressors. The study results showed that, whereas EAIC preferred the parsimonious order one auto -aggressive {AR(1)} structure for the data, QIC preferred the unstructured matrix which estimates the highest number of correlation parameters. Using the AR(1) structure and Algorithm 2, the GEE model chosen had higher efficiency compared to when QIC is used to select both the correlation matrix and regressors. Based on the results, the study concludes that adopting hybrid methods enhances efficiency of GEE estimators. On firm value, the study concludes that besides the elements in the Gordon -Constant growth model, the financial health of a firm is a vital indicator of value creation ability by firms.