We have a two-step estimation problem where the first step corresponds to the treatment model and the second to the outcome model. As shown in Using gmm to solve two-step estimation problems, this can be solved with the generalized method of moments using gmm. This continues the series of ...
To statalist@hsphsun2.harvard.edu Subject st: All two-step sysGMM coefficients are insignificant Date Fri, 06 Mar 2009 09:59:52 +0100Hello everybody, I have a balanced panel of 19 countries over 24 time periods. The model is g_it= ß_0+δ*g_(it-1)+ß_1*x_it+ε_it It is...
Windmeijer (2005) explains why, with numerous instruments, the estimated asymptotic standard errors of the efficient, two-step, GMM estimator are downward biased in small samples. GMM estimation is based on an estimated optimal weight matrix, which is the inverse of the covariance of the sample ...
Arellano and Bond (1991) develop new one-step and two-step GMM estimators for dynamic panel data. See [XT] xtabond for a discussion of these estimators and Stata's implementation of them. In their article, Arellano and Bond (1991) apply their new estimators to a model of dynamic labor ...
{phang}{help twostepweakiv##references:References}{p_end} {phang}{help twostepweakiv##citation:Citation of twostepweakiv}{p_end} {marker description}{...} {title:Description} {pstd} Building on the existing Stata package {helpb weakiv}, {opt twostepweakiv} construct tests for weak...
The biomass models mentioned above were constructed using Stata software (version 17), and the model parameters were estimated using the NSUR method within the program. (2) Generalized method of moments (GMM) GMM is a flexible estimation method that can be applied to a wide range of models ...
The differences between the observed values and one-step ahead forecasts are a highly relevant measure for assessing current inflation data. Thus, the comparison of this forecast error with the corresponding confidence interval allows us to detect whether there is any significant innovation in inflation...