Determinants of firm performance: How to choose the best model among panel data models?

Purpose The study aims to examine the determinants that affect the firm’s performance in the service industries. Moreover, the study examines step-by-step how to choose the best model among the various panel data models. Design/methodology/approach After employing the secondary data of the service industry firms from the CRIBIS database from 2017 to 2021, I estimated the regression model by pooled ordinary least squares (OLS), fixed-effect, one-step difference generalized method of moments (GMM), two-step difference GMM, one-step system GMM and two-step system GMM. Findings After employing the suggested criteria by Bond, Hoeffler, and Temple (2001), the findings of the current research exposed that the outcomes of the two-step system GMM should be preferred among dynamic panel data models. The significant results of the two-step system GMM revealed that leverage, tangibility, firm size and firm age negatively impact firm performance in the Czech service industries. Conversely, capital turnover ratio and physical capital intensity have a positive relationship with firm performance. Practical implications This research will help academics, policymakers and managers improve firm performance. The present research findings will guide them in choosing the appropriate model among dynamic panel data models and examining the factors that affect the firm’s performance in the service industries. Originality/value This is the first study to suggest how to choose the best model among various panel data models and then examine how a variety of determinants affect the firm performance of the service industries. By including six service sectors simultaneously, the study comprehensively answers how and why to choose an appropriate model among dynamic panel models and then interprets each of the selected determinants of firm performance from economic and financial points of view. Therefore, this study contributes both theoretically and practically to the field.