Panel data are repeated observations on the same cross section unit, typically of individuals or firms (in microeconomic applications), observed for several time periods. The use of panel data has been increasingly popular in empirical macroeconomic and (especially) microeconomic studies and there are several reasons behind the success story. This thesis analyses the properties of the estimation techniques for panel data models with additive and multiplicative error structures. First, this thesis discusses the relative merits of the maximum likelihood estimators in dynamic panel data models. Second, it provides an in-depth analysis of genuine and pseudo panel data models with unobserved interactive effects.