This paper considers generalized method of moments (GMM) and sequential GMM (SGMM) estimation dynamic short panel data models. The efficient motivated from the quasi maximum likelihood (QML) can avoid use many instrument variables (IV) for estimation. It be asymptotically as estimators (MLE) when disturbances are normal, more than QML not normal. SGMM, which also incorporates IVs, generalizes m...

This entry describes the basic framework for statistical estimation and inference using Generalized Method of Moments and also illustrates the types of empirical models in finance to which these techniques have been applied. GeneralizedMethod of Moments (GMM) provides a computationally convenientmethod of obtaining consistent and asymptotically normally distributed estimators of the parameters ...

This paper investigates the impact of stock markets and banks on economic growth using a panel data set for the period 1976-98 and applying recent GMM techniques developed for dynamic panels. Econometrically, the paper illustrates the differences that emerge from different panel procedures. On balance, we find that stock markets and banks positively influence economic growth and these findings ...

We present estimators for panel data sample selection and switching models where the regression equations are dynamic and it is allowed for the existence of endogenous regressors and correlated individual unobserved heterogeneity. We consider two types of switching models under the names of observed dynamics switching and latent dynamics switching. The dynamic sample selection model implicitly ...

This paper explains why Godambe-Durbin \estimating functions" (EFs) from 1960 are worthy of attention in econometrics. Godambe and Kale (1991) show the failures of Gauss-Markov and least squares and prove the small-sample superiority of EFs. There are many areas of Econometrics including unit root estimation, generalized method of moments (GMM), panel data models, etc., which can use some simpl...

The two-step GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) for dynamic panel data models have been widely used in empirical work; however, neither of them performs well in small samples with weak instruments. The continuous-updating GMM estimator proposed by Hansen, Heaton, and Yaron (1996) is in principle able to reduce the small-sample bias, but it involves high-dime...

In this chapter we study GMM estimation of linear panel data models. Several different types of models are considered, including the linear regression model with strictly or weakly exogenous regressors, the simultaneous regression model, and a dynamic linear model containing a lagged dependent variable as a regressor. In each case, different assumptions about the exogeneity of the explanatory v...