Generalized Method of Moments (GMM) provides a computationally convenient method for estimating the parameters of statistical models based on the information in population moment conditions. This structure has made it very popular in econometrics because competing economic theories often imply that economic variables satisfy different sets of population moment conditions. The specific form of these population moment conditions depends on the context but the generic form of the GMM estimator is the same in each case. This flexibility means that GMM has been implemented in very diverse areas spanning macroeconomics, finance, agricultural economics, environmental economics and labour economics. Its widespread use in econometrics has both stimulated and been facilitated by the development of numerous statistical inference techniques based on GMM estimators. These inference techniques allow researchers, inter alia, to test hypotheses about the parameters of the econometric model and also to test whether the population moment conditions are consistent with the data. In addition, GMM subsumes many other well-known estimators, such as least squares, instrumental variables and maximum likelihood. As a result, GMM provides a convenient framework for considering general aspects of estimation and inference in statistics, and, in many ways, is becoming the common language of econometric dialogue.
Hello sir, I am Rahma, 2nd-year student at the Monastir University, Mahdia, Tunisia. The problem is that my database of 30 African countries from 1980 to 2017 can not be included in the STAT program. But next command that I included in STATA forvall i = 1/30 { ardl dep var indep var if (Name_country == "i" "), maxlag (p, q, ....) matrix list e (lags) di }
Generalized Method of Moments (GMM) provides a computationally convenient method for estimating the parameters of statistical models based on the information in population moment conditions. This structure has made it very popular in econometrics because competing economic theories often imply that economic variables satisfy different sets of population moment conditions. The specific form of these population moment conditions depends on the context but the generic form of the GMM estimator is the same in each case. This flexibility means that GMM has been implemented in very diverse areas spanning macroeconomics, finance, agricultural economics, environmental economics and labour economics. Its widespread use in econometrics has both stimulated and been facilitated by the development of numerous statistical inference techniques based on GMM estimators. These inference techniques allow researchers, inter alia, to test hypotheses about the parameters of the econometric model and also to test whether the population moment conditions are consistent with the data.
In addition, GMM subsumes many other well-known estimators, such as least squares, instrumental variables and maximum likelihood. As a result, GMM provides a convenient framework for considering general aspects of estimation and inference in statistics, and, in many ways, is becoming the common language of econometric dialogue.
great lecture
Can somebody explain more detailed, why it is a problem if the spatial coefficients in the SARSAR model have the same sign (1:00:49-1:01:16)?
Hello sir,
I am Rahma, 2nd-year student at the Monastir University, Mahdia, Tunisia. The problem is that my database of 30 African countries from 1980 to 2017 can not be included in the STAT program. But next command that I included in STATA
forvall i = 1/30 {
ardl dep var indep var if (Name_country == "i" "), maxlag (p, q, ....)
matrix list e (lags)
di
}
or GMM estimations
cov(endogenous variable, error term)=0, no correlation.
weighting matrix. inv of variance matrix (diagonal?)