Charts of the 10% and 50% Points of the Operating Characteristic Curves for Fixed Effects Analysis of Variance F Tests, α = 0.01 and 0.05
固定效应方差分析 F 检验操作特征曲线的 10% 与 50% 点图表AI 解读 ↓
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固定效应方差分析 F 检验操作特征曲线的 10% 与 50% 点图表AI 解读 ↓
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TABLES of the power functions for fixed effects analysis of variance F tests have been published by Tang [4 ] and Lehmer [2 ] and charts of these power functions have been presented by Pearson and Hartley [3 ] and Fox [1 ]. Pearson and Hartley offer eight charts of power curves for a = 0.01 and a = 0.05 and degrees of freedom fi from 1 to 8 and f2 from 6 to oc. They cover eight pages, one for each value of fi. Fox gives lines of constant ( (defined below) for P = 0.5, 0.7, 0.8 and 0.9 and a = 0.01 and 0.05, and values of f, from 3 to X and values of f2 from 4 to oo. Single charts are for a specified value of a and P.
伪面板事件研究:可信应用的计量经济学基础
Event studies with panel data are widely used to estimate treatment effects. With repeated cross-sections, future treatment dates are unobserved for untreated individuals. Pseudo-panel event studies impute pre-treatment outcomes of the treated using matched untreated individuals. This paper establishes identification in such designs and characterizes bias when identification conditions fail. I show that the pseudo-panel regression is equivalent to weighted least squares with…
双重高维线性混合模型下异质图模型的推断
Inferring the structure of population-level functional connectivity networks from multi-subject functional magnetic resonance imaging data is essential for understanding the connectivity patterns underlying cognitive and behavioral functions. Graphical models provide a formal statistical framework for inferring the graph structure. However, the multi-level nature of functional magnetic resonance imaging data---where each level corresponds, for instance, to a…
高维近似因子时间序列模型的主成分分析:假设、渐近理论与识别
We consider estimation of large approximate factor models in high-dimensional panels of stationary time series using Principal Component Analysis (PCA). We review the key results establishing the necessary and sufficient conditions for consistency and asymptotic normality of the estimators, which hold when both the cross-sectional dimension $n$ and the sample size $T$ tend to infinity. Special emphasis is placed on identification. First, we show that the common and…
面板数据分位数回归的GMM方法
This paper develops a smoothed generalized method of moments (GMM) framework for panel quantile models with individual-specific fixed-effects and endogenous regressors. By smoothing the indicator in the quantile moment conditions and profiling out the individual effects through smoothed individual quantile restrictions, the proposed estimator can be implemented using standard GMM algorithms. We establish consistency, asymptotic normality, and feasible inference under weak…
面板信息矩阵检验
The Information Matrix (IM) test is a natural specification check for likelihood-based models, yet cross-sectional implementations are often badly sized, confound neglected heterogeneity with distributional misspecification, and require third derivatives. We develop the panel information matrix (PIM) test for models with fixed effects. Explicit fixed effects isolate functional-form misspecification from time-invariant unobserved heterogeneity. Although the profile maximum…
具有双向依赖的面板结构VAR推断
We develop inference for heterogeneous panel vector autoregressive (VAR) models and their structural impulse response functions, where the error terms are dependent in the cross-sectional and time dimensions (two-way dependence). For proxy-identified structural VARs, we first adapt mean-group estimation and construct a closed-form pooled identification. Considering the reduced-form VAR dynamics, residual covariances, and structural parameters, we derive a joint central limit…