The Curious Case of Aid and Conflict: Causal Evidence from Panel Econometrics and Composite Indices
援助与冲突的复杂关系:面板计量与综合指数的证据AI 解读 ↓
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援助与冲突的复杂关系:面板计量与综合指数的证据AI 解读 ↓
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This paper examines the relationship between Official Development Assistance (ODA) and conflict in the ten largest aid-receiving African countries between 2009 and 2023. Using Ordinary Least Squares, Principal Component Analysis, and Ridge (L2) regression, the study assesses whether conflict, proxied by political stability, governance indicators, and macroeconomic conditions, systematically influences aid inflows. Results reveal a nuanced relationship. Pooled regressions indicate that aid is positively associated with poverty, inflation, and fragility, while voice and accountability are negatively related to ODA. Fixed-effects estimates instead show positive associations between aid, political stability, and GDP per capita over time, alongside negative correlations with perceived corruption. Ridge regression confirms the robustness of various governance variables under multicollinearity. Overall, donors appear responsive to both humanitarian need and institutional quality, producing an aid-conflict-institutions trilemma: aid is most concentrated where conflict risk and institutional weakness are greatest, yet these same conditions which constrain aid effectiveness. The paper contributes by integrating theory with panel-econometric tools to to explore international development aid allocation.
经济增长建模的贝叶斯方法:变量选择与截面依赖
This paper introduces a Bayesian framework designed to address two critical econometric challenges in empirical growth studies: variable selection and cross-sectional dependence. We first propose a growth regression model that incorporates cross-sectional dependence through a multi-factor structure and accounts for variable selection with sparse coefficients. We then introduce a new Bayesian Adaptive LASSO estimator specifically designed for this model, capable of performing…
具有潜在分组结构与截面依赖的非平稳面板模型
This paper proposes a novel Lasso-based approach to handle unobserved parameter heterogeneity and cross-section dependence in nonstationary panel models. In particular, a penalized principal component (PPC) method is developed to estimate group-specific long-run relationships and unobserved common factors and jointly to identify the unknown group membership. The PPC estimators are shown to be consistent under weakly dependent innovation processes. But they suffer an…
ArCo:高维面板时间序列的人工反事实方法
We consider a new, flexible and easy-to-implement method to estimate thecausal effects of an intervention on a single treated unit when a control group is not available and which nests previous proposals in the literature. It is a two-step methodology where in the first stage, a counterfactual is estimated based on a large-dimensional set of variables from a pool of untreated units by means of shrinkage methods, such as the least absolute shrinkage and selection operator…
含交互固定效应与多重结构突变的面板数据模型
In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the…
交互固定效应面板中潜在组结构的收缩估计与识别
This paper provides a framework for joint shrinkage estimation and identification of latent group structures in panel data models with interactive fixed effects and large number of explanatory variables. The latent structure of the model allows individuals to be classified into a number of groups where the number of groups and/or each individual’s group identity are unknown. A doubly penalized principal component estimation procedure using a pairwise fusion penalty and an…
多重结构突变与多因子误差下空间面板模型的收缩估计
This study investigates spatial panel data models with a multifactor error structure and multiple structural breaks occurring in the coefficients of both spatial lagged and explanatory variables. While extensive research has addressed cross-sectional dependence in panel data, including approaches that integrate spatial and factor structures within a single framework, few studies account for time-varying model parameters and achieving consistent estimation remains a…