This paper considers first-order autoregressive panel model which is a simple model for dynamic panel data (DPD) models. The generalized method of moments (GMM) gives efficient estimators for these models. This efficiency is affected by the choice of the weighting matrix which has been used in GMM estimation. The non-optimal weighting matrices have been used in the conventional GMM estimators. ...

Generalized method of moments (GMM) is used to develop tests for discriminating discrete distributions among the two-parameter family of Katz distributions. Relationships involving moments are exploited to obtain identifying and over-identifying restrictions. The asymptotic relative e5ciencies of tests based on GMM are analyzed using the local power approach and the approximate Bahadur e5ciency...

This paper shows how to estimate models by the generalized method of moments and the generalized empirical likelihood using the R package gmm. A brief discussion is offered on the theoretical aspects of both methods and the functionality of the package is presented through several examples in economics and finance.

JEL Classification G32, G34, G39. This paper examines the role of debt resulting from bank financing for corporate governance towards the activities of withdrawing cash and assets to controlling shareholders’ account to benefit their rights by capturing secondary data for 250 nonfinancial firms listed in the Vietnam Index, considered as one of the emerging stock markets, during the period from ...

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 ...

The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to th...

A novel approach to the combination of volatility forecasts is discussed. The proposed procedure makes use of the generalized method of moments (GMM) for estimating the combination weights. The asymptotic properties of the GMM estimator are derived while its finite sample properties are assessed by means of a simulation study. The results of an application to a time series of daily returns on t...

This paper investigates a generalized method of moments (GMM) approach to the estimation of autoregressive roots near unity with panel data and incidental deterministic trends. Such models arise in empirical econometric studies of Þrm size and in dynamic panel data modeling with weak instruments. The two moment conditions in the GMM approach are obtained by constructing bias corrections to the ...