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层级多因子误差下多维异质面板的估计与推断AI 解读

Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-section dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach…

具有截面依赖的随机前沿面板中的技术效率建模AI 解读

Summary This paper proposes a unified framework for accommodating both time and cross‐sectional dependence in modelling technical efficiency in stochastic frontier models by combining the exogenously driven factor‐based approach and an endogenous threshold efficiency regime selection mechanism. This approach is able to deal with both weak and strong cross‐sectional dependence in a flexible manner. Using a dataset of 26 OECD countries over 1970–2010, we provide satisfactory…

欧盟内部贸易引力模型：异质共同因子面板中的 CCEP-HT 估计AI 解读

Abstract We follow recent developments of panel data studies and allow for the existence of both observed and unobserved common factors where their individual responses are allowed to be heterogeneous. We then develop a generalized Hausman–Taylor estimation methodology, and apply our proposed estimation technique to an analysis of the gravity equation of bilateral trade flows among 15 European countries over 1960–2001. Empirical results demonstrate that our proposed approach…