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含高阶网络相互依赖的多元面板Probit贝叶斯估计：广东企业全球市场参与AI 解读

Summary This paper proposes a Bayesian estimation framework for panel data sets with binary dependent variables where a large number of cross‐sectional units are observed over a short period of time and cross‐sectional units are interdependent in more than a single network domain. The latter provides for a substantial degree of flexibility towards modeling the decay function in network neighborliness (e.g., by disentangling the importance of rings of neighbors) or towards…

同时含时变与时间不变解释变量的若干非线性面板模型估计AI 解读

The so-called “fixed effects” approach to the estimation of panel data models suffers from the limitation that it is not possible to estimate the coefficients on explanatory variables that are time-invariant. This is in contrast to a “random effects” approach, which achieves this by making much stronger assumptions on the relationship between the explanatory variables and the individual-specific effect. In a linear model, it is possible to obtain the best of both worlds by…

中国化工行业的企业生产率溢出：空间 Hausman–Taylor 方法AI 解读

Summary This paper assesses intra‐sectoral spillovers in total factor productivity (TFP) across Chinese producers in the chemical industry using panel data on 12,552 firms observed over 2004‐2006. We specify TFP as a composite of observable factors such as export market participation, foreign as well as public ownership, the extent of accumulated intangible assets, and unobservable components. We introduce spatial spillovers in TFP through contextual effects of observable…