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9 results on '"Ni, Mingming"'

4. Impact of rural capital outflow on sustainable economic growth: evidence from Shaanxi Province of China

Author

Li, Jian, Ni, Mingming, Li, Zhiyi, Li, Jian, Ni, Mingming, and Li, Zhiyi

Abstract

The capital constraint and underinvestment faced by rural residents are the pivotal factors limiting economic growth. The rural capital outflow further magnifies the income gap between urban and rural residents, seriously undermining rural areas’ capital accumulation and sustainable development. For this purpose, we perform a theoretical and empirical investigation of the impact of rural capital outflow on economic growth. The results reveal that rural capital outflow occupies investment opportunities for rural economic development that adversely affect economic growth. Secondly, rural economic growth has an inertial effect and generates path dependence on capital outflow. The calculation of rural capital outflow in northern Shaanxi, Guanzhong, and southern Shaanxi regions of China reveals that capital outflow in northern Shaanxi is insignificant and shows signs of capital return, while southern Shaanxi and Guanzhong emerge as key regions for capital outflow.Policymakers must adhere to long-term initiatives to reform and refine multi-level capital markets that support agricultural development in rural areas and build a sound rural financial market order for sustainable economic growth.

Published
2023

8. Growth curves mixture model with serial covariance structure

Author

Fei Yu, Pan Jianxin, Pan Yating, and Ni Mingming

Subjects
Normal distribution, Estimation theory, General Mathematics, Expectation–maximization algorithm, Design matrix, Covariance, Mixture model, Cluster analysis, Growth curve (statistics), Algorithm, Mathematics
Abstract

Growth curve models (GCMs) play an important role in longitudinal data analysis. However, a very crucial assumption is that the grouping design matrix is assumed to be known, which may not be true in practice. In this paper, we propose a novel mixture model which can handle the problem caused by the unknown grouping matrix. In the meantime, it approximates the true distribution of the response matrix using a mixture of normal distributions. The new model is studied under the serial covariance structure (SCS), which accounts for serial correlation among different observations within the same individual. The maximum likelihood estimation for the proposed model is considered by the use of EM (expectation maximization) algorithm, along with clustering individuals. A data-driven method is used to find a appropriate model. Simulation studies and real data analysis show that the proposed procedure works well in parameter estimation and individual clustering, simultaneously.

Published
2020

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