Estimation of Panel Data Models with Individual Effects

This book focused on the panel data model estimators. Panel data is a continuously developing field. Panel data combine cross-sectional and time-series data and, therefore, provide a more appealing structure of data analysis than either cross sectional or time-series data, alone. Panel data analysis has many advantages over analysis using time-series and cross-sectional data alone. For example, the increased sample size due to the utilization of cross-sectional and time-series data improves the accuracy of model parameters' estimates due to a greater number of degrees of freedom and less multicollinearity compared to either cross-section or time-series data alone. Additionally, since panel data contain information on both the inter-temporal dynamics and the individuality of entities, it controls for the effect of missing variables on the estimation results. This book targeted at undergraduate, postgraduate students and other researchers who want to further their study on panel data models.