GMM estimation of partially linear single-index spatial autoregressive model

Cheng, Suli; Chen, Jianbao; Li, Xuan

doi:10.1016/j.spasta.2019.04.002

Cited by 21 publications

(4 citation statements)

References 23 publications

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“…The specific index definition and statistical description are shown in Table 1 and Table 2. Before the estimation of panel data model, in order to avoid the phenomenon of "pseudo regression", it is necessary to test the stationarity of each variable [20][21]. The use of non-stationary variables for panel data model estimation cannot accurately describe the logical relationship between variables [22].…”

Section: Unit Root Testmentioning

confidence: 99%

Relationship Between Energy Consumption and Industrial Output-Based on GMM Model of Dynamic Panel Data in China

Baharum

Jin

2022

ARASET

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At present, China is in the historical intersection period of the "two centenary" goals. The realization of the "carbon neutral" goal has brought new opportunities for the development of China's energy sector. Based on the GMM estimation method of the dynamic panel model, this paper studies the relationship between energy consumption and industrial output by using China's provincial data from the years 2008 to 2018. The results show that the impact of coal consumption on industrial output tends to be declining during the sample period. However, compared with other energy consumption, it still dominates a larger proportion. Oil consumption has a negative impact on industrial output with the arrival of a production bottleneck period and the improvement of technology level. As a secondary energy, electricity consumption has an important impact on the industrial output, which is the main energy variable and is significantly positive. Other energy consumption variables have significant positive effects on industrial output. In this paper, we obtain countermeasures and suggestions on energy, such as giving full play to the decisive role of the market, improving the innovation environment in the field of energy, speed up the transformation of new and old kinetic energy.

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Section: Unit Root Testmentioning

confidence: 99%

Relationship Between Energy Consumption and Industrial Output-Based on GMM Model of Dynamic Panel Data in China

Baharum

Jin

2022

ARASET

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“…Du et al [8] proposed the estimator for the asymptotic covariance matrix of the parameter estimator of partially linear additive SAR models and established the asymptotic properties for the resulting estimators. Other research results on SAR models can also be referred to Cheng et al [9], Dai et al [10], Gupta and Robinson [11], Lin and Lee [12], Tian et al [13], Tian et al [14], and so on. Tese studies based on cross-sectional data are not applicable to panel data, and variable selection is rarely involved.…”

Section: Introductionmentioning

confidence: 99%

Variable Selection of High-Dimensional Spatial Autoregressive Panel Models with Fixed Effects

Xia

Zhang

Tian

2023

Journal of Mathematics

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This paper studies the variable selection of high-dimensional spatial autoregressive panel models with fixed effects in which a matrix transformation method is applied to eliminate the fixed effects. Then, a penalized quasi-maximum likelihood is developed for variable selection and parameter estimation in the transformed panel model. Under some regular conditions, the consistency and oracle properties of the proposed estimator are established. Some Monte-Carlo experiments and a real data analysis are conducted to examine the finite sample performance of the proposed variable selection procedure, showing that the proposed variable selection method works satisfactorily.

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“…Finally, any econometric model for which orthogonal conditions can be established can be estimated using the GMM method. The externality of instrumental variables and their correlation with endogenous variables are two very important assumptions for the use of GMM in empirical studies [ 26 ]. However, if there is only a weak correlation between the instrumental variable and the endogenous variable, a series of weak instrumental variable problems will occur.…”

Section: Introductionmentioning

confidence: 99%

Impact Analysis of Environmental Regulation and Improvement of Agricultural Economic Efficiency on Living Environment Based on Systematic GMM Model

Zhu

Zhou

2022

Journal of Environmental and Public Health

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The living environment is of vital importance to human beings and is also the basis for their survival and development. Along with the accumulation of people’s production and living experience, people are seeking their own development while studying the living environment more deeply. In recent years, various regions and departments have conscientiously implemented the relevant decisions and deployments. They have promoted the construction of rural infrastructure and the equalisation of basic public services between urban and rural areas, so that the rural living environment can be gradually improved. The rapid development of urban-rural integration has brought great opportunities and challenges for rural development, making rural development issues increasingly complex and affecting the effectiveness of the construction of a new rural habitat. At the same time, the agricultural economy is the basis of national economic development, and the ecological environment provides a certain amount of material environment for the development of the agricultural economy. Therefore, research on the harmonious development of the agricultural economy and the ecological environment has always been a hot issue for the authorities concerned. After all, China’s agricultural economic development mode is characterized by a rough-and-ready approach, and the agroecological environment continues to deteriorate, leaving the issue of sustainable agricultural development to be resolved. What is worse, in the practice of agricultural production, the related agricultural activities have brought serious pollution to the environment. Specifically, the carrying capacity of the agricultural ecological environment is declining, leading to an increasingly serious conflict between sustainable agricultural economic development and the construction of an environmentally friendly and resource-saving society. In China, although the industrialization process is accelerating, the agricultural population still accounts for a large proportion of the population. As people’s living standards continue to improve, a favorable living environment has become an urgent need for farmers. As a result, strengthening the construction of rural infrastructure, improving rural living conditions, and improving rural environmental sanitation are important issues that need to be addressed urgently. Therefore, to further investigate the impact of environmental regulation and agricultural economic efficiency on the habitat, this study constructs a systematic GMM model using panel data for 30 provinces and regions in China from 2011 to 2021.

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GMM estimation of partially linear single-index spatial autoregressive model

Cited by 21 publications

References 23 publications

Relationship Between Energy Consumption and Industrial Output-Based on GMM Model of Dynamic Panel Data in China

Relationship Between Energy Consumption and Industrial Output-Based on GMM Model of Dynamic Panel Data in China

Variable Selection of High-Dimensional Spatial Autoregressive Panel Models with Fixed Effects

Impact Analysis of Environmental Regulation and Improvement of Agricultural Economic Efficiency on Living Environment Based on Systematic GMM Model

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