Interactive effects of mixtures of stimuli in life table analysis
生命表分析中混合刺激的交互效应AI 解读 ↓
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生命表分析中混合刺激的交互效应AI 解读 ↓
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An interaction is a response to a mixture of stimuli that, based on the responses to the individual stimuli, seems unexpectedly large or small. The conclusions about the presence of interactions can depend upon the chosen parameterization. For dichotomous outcome variables, a simple probabilistic model of no interaction exists (Finney, 1952). In this model, individuals are at risk from challenges from the background and the separate stimuli, all acting independently. In this paper, this model is extended to survivorship data. To test the hypothesis of no interaction, a likelihood ratio statistic and a statistic based on a standardized interaction are proposed for grouped-time data. A statistic in the spirit of Mantel—Haenszel is constructed for continuous-time data. Asymptotic relative efficiencies of these tests versus the parametric likelihood ratio test are calculated. A limited Monte Carlo study is performed to investigate the small sample properties.
高维面板数据模型的稳健估计与推断
This paper provides the relevant literature with a complete toolkit for conducting robust estimation and inference about the parameters of interest involved in a high-dimensional panel data framework. Specifically, (1) we allow for non-Gaussian, serially and cross-sectionally correlated and heteroskedastic error processes, (2) we develop an estimation method for the high-dimensional long-run covariance matrix using a thresholded estimator, (3) we also allow for the number of…
重审产出收敛争论:面板数据模型分析新进展的启示
This paper provides a critical examination of the empirical basis of the output convergence debate in the light of recent developments in the analysis of dynamic heterogeneous panels with interactive effects. It shows that popular tools such as Barro's cross-country regressions and two-way fixed effects (TWFE) estimators that assume parallel trends and homogeneous dynamics lead to substantial under-estimation of the speed of convergence and misleading inference. Instead,…
交互效应异质性空间动态面板模型的IV估计
This paper develops a Mean Group Instrumental Variables (MGIV) approach for spatial dynamic panel data models with interactive effects, under large N and T asymptotics. Unlike existing approaches that typically impose slope-parameter homogeneity, MGIV accommodates cross-sectional heterogeneity in slope coefficients. The proposed estimator is linear, making it computationally efficient and robust. Furthermore, it enables asymptotically valid inferences without requiring bias…
基于离散傅里叶变换检验交互固定效应面板模型的结构变化
We propose a discrete Fourier transform (DFT)-based test for detecting structural changes in panel data models with interactive fixed effects (IFE). When structural changes are present but ignored, the conventional asymptotic distribution of the least squares (LS) estimator of Bai (2009) may no longer provide reliable inference, and the resulting residuals contain signals of parameter instability. Our test exploits this feature by comparing the DFT of weighted residuals to…
We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., a factor structure). We show that previously developed estimators and confidence intervals (CIs) are heavily biased and size distorted when some of the factors are weak. Combining the theory of minimax linear estimation with a nuclear norm bound on the error of an initial estimate of the interactive effects, we propose estimators with improved rates of…
非线性面板数据模型的低秩估计
This paper investigates nonlinear panel models with interactive fixed effects and introduces a general framework for parameter estimation under potentially nonconvex objective functions. We propose a computationally feasible two-step estimation procedure. In the first step, nuclear-norm regularization (NNR) is used to obtain preliminary estimators of the coefficients of interest, factors, and factor loadings. The second step involves an iterative procedure for post-NNR…