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Analyzing entrepreneurship-growth nexus across high and low income countries
Published in Zoltán Bartha, Tekla Szép, Katalin Lipták, Dóra Szendi, Entrepreneurship in the Raw Materials Sector, 2022
Arellano and Bond (1991) Dynamic Panel Data approach was popularized in the work of Holtz-Eakin, Newey and Rosen (Econometrica, 1988). It is based on the idea that the instrumental variables approach does not exploit all of the information available in the sample. In that effect, the Generalized Method of Moments (GMM) construct more efficient estimates of the dynamic panel data model. Arellano and Bond argue that consistency, fails to take all of the potential orthogonality conditions into account. Notably, there is the assumption that the necessary instruments are ‘internal’: that is, based on lagged values of the instrumented variable(s). The estimators allow the inclusion of external instruments as well. Arellano and Bond (1991) therefore presented a panel data analysis based on a GMM-type estimator called the “system estimator”, to answer some of the potential econometric problems that emanates with working with DPD.
The structure and performance of U.S. research joint ventures: inferences and implications from the Advanced Technology Program
Published in Cristiano Antonelli, Albert N. Link, Assessing Technology and Innovation Policies, 2020
James D. Adams, Albert N. Link
Table 3 concludes with (3.3) and (3.4). These are IVs results that use generalized method of moments (GMM) to handle endogeneity of project budget per firm and additional money invested by the firm. Endogeneity could arise for project budget if larger projects are awarded to firms that invent more, instead of firms inventing more because of a larger budget. Also, more inventive projects encourage firms to invest more, rather than the reverse, causing additional money invested to be endogenous. For both, endogeneity could lead to correlations with the equation errors. Project budget per firm is the more important, since additional money is not significant in (3.1) and (3.2).
Economic Development, Environmental Degradation and Sustainability
Published in Uday Chatterjee, Arindam Biswas, Jenia Mukherjee, Dinabandhu Mahata, Sustainable Urbanism in Developing Countries, 2022
Nilendu Chatterjee, Bappaditya Koley, Anindita Nath, Uday Chatterjee
We also have applied panel unit root tests for smooth supervision or conduction of estimation. The generalized method of moments (GMM)8 has been used to estimate the relationships with the help of simultaneous equations. Again, panel unit root tests have been applied for the overall panel. The following panel unit root tests have been applied: LLC test (Levin et al., 2002), IPS test (Im et al., 2003), PP-Fisher chi-square (Maddala & Wu, 1999) and Fisher-type augmented Dicky Fuller (ADF) test.
Revisiting the nexus between financial agglomeration and energy efficiency: A spatial spillover approach
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2022
Fengrong Wang, Chenxi Zhang, William Mbanyele, Hongyun Huang, Tomas Balezentis
Second, previous studies (e.g., Wang and Wang 2020; Wu, Hao, and Ren 2020) argued that China’s EE is characterized by strong persistence, such that static panel models may fail to capture this time-lag effect, thereby producing inconsistent and misleading results. To address this concern, we employed the System Generalized Method of Moments (Arellano and Bover 1995; Blundell and Bond 1998) and Differential Generalized Method of Moments (Arellano and Bond 1991) approaches to reexamine the causal relationship between FA and EE. The regression models (11) and (12) in Table E1 present the main estimation results. The results of the Hansen test in regression (11) and (12) are insignificant at 10% level, implying the validity of the instrumental variable setting in the GMM estimation. Furthermore, we find that the coefficients of FA and its squared term are still positive and negative and significant at the 1% and 5% levels, respectively.
The spatial effect of factor market distortion on green agriculture development in China
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022
Xinming Wang, Chao Hua, Jianjun Miao
In order to eliminate the endogenous influence, the panel instrumental variable method could be introduced to further test the relationship between the two agricultural substances and factor market distortions. This article takes two factor market distortions as endogenous control variables. The selection of instrumental variables needs to follow the requirements of relevance and exogeneity. Lagging variables are significantly correlated with endogenous control variables, and have nothing to do with random disturbance items. Therefore, lagging variables are often selected as instrumental variables for experimentation. Referring to the method of Shi and Xian (2012), the first-order lag items of two factor market distortions are selected as instrumental variables. The generalized method of moments (GMM) can be used to estimate the regression coefficients of factor market distortions. The regression results in Table 7 denote that under the instrumental variable method, the signs of the regression coefficients of the two factor market distortions are consistent with the results of the previous spatial econometric regression. The two factor market distortions have a stable inverted U-shaped impact on both FU and PU.
Impact of financial structure on environmental quality: evidence from panel and disaggregated data
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2019
Kizito Uyi Ehigiamusoe, Vinitha Guptan, Hooi Hooi Lean
Firstly, we employ the dynamic panel generalized method of moments (GMM) estimator proposed by Arellano and Bond (1991) to estimate the impact of the financial development on environmental quality. The GMM technique can control for country-specific effect, endogeneity and autocorrelation. We conduct two tests to determine the consistency of the GMM estimator. First, we use the Sargan test of over-identifying restriction to test the joint validity of the instruments. Second, we use the Arellano and Bond test for autocorrelation to test for the presence of second order serial correlation. Since there are two variants of GMM estimator namely one-step and two-step procedures, we use the two-step estimator since it is more asymptotically efficient than the one-step estimator. We follow the approach of Windmeijer (2005) to compute robust two-step standard errors.