One of the most popular methods of estimation of econometric models is Ordinary Least Squares (OLS). The desired properties of the OLS estimators depend on whether the method's assumptions are true. If not, another method of estimation should be applied. An universal method, that can be applied to models, in which the normality of the error term distribution is not assumed, is the Generalized Method of Moments (GMM). One of the main advantages of GMM is that it can be used to perform inference about the parameters in nonlinear dynamic models. The main goal of this paper is to present the key elements of GMM, and one of the possibilities of using it for inference in dynamic panel models
In analyses based on time-series - cross-section data (TSCS) the following problems are very often encountered: groupwise heteroscedasticity, cross-sectional correlation and autocorrelation of the error term, the latter being common for all units or unit-specific. There are different ways of taking those phenomena into account while hypotheses testing and estimating a model. The estimation methods, that are most often used are FGLS (adapted for this case by Parks) and OLS with panel corrected standard errors, PCSE proposed by Beck and Katz. Both these methods are applied to the analysis of labour demand by (chosen) PKD sections and their effectiveness is examined.
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