论文检索

面向处理效应异质性学习的自适应实验AI 解读

Abstract Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in this research area focused on either analysing observational data based on strong causal assumptions or conducting post hoc analyses of randomized controlled trial data, and there has been limited effort dedicated to the…

半参数后验修正AI 解读

Abstract We present a new approach to semiparametric inference using corrected posterior distributions. The method allows us to leverage the adaptivity, regularization, and predictive power of nonparametric Bayesian procedures to estimate low-dimensional functionals of interest without being restricted by the holistic Bayesian formalism. Starting from a conventional posterior on the whole data-generating distribution, we correct the marginal posterior for each functional of…

两阶段拒绝式抽样及其渐近性质AI 解读

Abstract Rejective sampling improves design and estimation efficiency of single-phase sampling when auxiliary information in a finite population is available. When such auxiliary information is unavailable, we propose to use two-phase rejective sampling (TPRS), which involves measuring auxiliary variables for the sample of units in the first phase, followed by the implementation of rejective sampling for the outcome in the second phase. We explore the asymptotic design…

一般协方差下超高维均值的随机化经验似然检验AI 解读

Abstract This article proposes a calibrated empirical likelihood test for ultra-high dimensional means that incorporates multiple projections. Under weak moment conditions on the distributions of data, we analyse all possible asymptotic distributions of the proposed test statistic in different scenarios. To determine the critical value and enhance test power, we employ the random symmetrization method based on the group of sign flips and use multiple selected projections.…

高斯变分近似中Cholesky因子的解析自然梯度更新AI 解读

Abstract Natural gradients can improve convergence in stochastic variational inference significantly but inverting the Fisher information matrix is daunting in high dimensions. Moreover, in Gaussian variational approximation, natural gradient updates of the precision matrix do not ensure positive definiteness. To tackle this issue, we derive analytic natural gradient updates of the Cholesky factor of the covariance or precision matrix and consider sparsity constraints…

校准参数的标准误AI 解读

Abstract Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate of the correlation structure of the empirical moments, which is often unavailable in practice. Instead, the variances of the individual empirical moments are usually readily estimable. Using only these variances, we derive…

平均处理效应的回归调整插补估计AI 解读

Imputing missing potential outcomes using an estimated regression function is a natural idea for estimating causal effects. In the literature, estimators that combine imputation and regression adjustments are believed to be comparable to augmented inverse probability weighting. Accordingly, people for a long time conjectured that such estimators, while avoiding directly constructing the weights, are also doubly robust (Imbens, 2004; Stuart, 2010). Generalizing an earlier…

半参数单指标变换模型的识别与推断AI 解读

This paper considers a semiparametric single index model in which the dependent variable is subject to a nonparametric transformation. The model has the form G0(Y)=g0(X⊤θ0)+e, where X is a random vector of regressors, Y is the dependent variable and e is the random noise, the monotonic function G0, the smooth function g0 and the index vector θ0 are all unknown. This model is quite general in the sense that it nests many popular regression models as special cases. We first…

不依赖序列排序的得分驱动动态因子模型AI 解读

This paper introduces a novel score-driven dynamic factor model designed for filtering cross-sectional co-movements in panels of time series. The model is formulated using elliptical distribution for noise terms, allowing the update of the time-varying parameter to be potentially nonlinear and robust to outliers. We derive stochastic properties of time series generated by the model, such as stationarity and ergodicity, and establish the invertibility of the filter. We prove…

用于预测组合的 Bregman 模型平均AI 解读

We propose a unified model averaging (MA) approach for a broad class of forecasting targets. This approach is established by minimizing an asymptotic risk based on the expected Bregman divergence of a combined forecast, relative to the optimal forecast of the forecasting target, under local(-to-zero) asymptotics. It can be flexibly applied to develop effective MA methods across various forecasting contexts, including but not limited to univariate and multivariate mean…

条件函数稀疏性下大型协方差矩阵函数的因子引导估计AI 解读

This paper addresses the fundamental task of estimating covariance matrix functions for high-dimensional functional data/functional time series. We consider two functional factor structures encompassing either functional factors with scalar loadings or scalar factors with functional loadings, and postulate functional sparsity on the covariance of idiosyncratic errors after taking out the common unobserved factors. To facilitate estimation, we rely on the spiked matrix model…

含结构变化的空间面板数据模型AI 解读

Spatial panel data models have gained widespread application in social sciences, particularly in economics, due to their ability to capture spatial dependencies. However, existing studies on spatial models typically rely on the assumption of parameter stability, which may be overly restrictive given the well-documented prevalence of structural changes in economic relationships. This paper addresses this limitation by proposing and analyzing spatial panel data models that…

基于重加权 CUSUM 与否决式决策规则的快速在线变点检测AI 解读

We study on-line changepoint detection in the context of a linear regression model, developing two novel contributions. Firstly, we propose a class of heavily weighted statistics based on the CUSUM process of the regression residuals, which are specifically designed to ensure timely detection of breaks occurring early on during the monitoring horizon. Secondly, we develop a class of composite statistics using different weighting schemes; the decision rule to mark a…

回归不连续设计的统一诊断检验AI 解读

Diagnostic tests for regression discontinuity design face a size-control problem. We document a massive over-rejection of the diagnostic restriction among empirical studies in the top five economics journals. At least one diagnostic test was rejected for 19 out of 59 studies, whereas less than 5% of the collected 787 tests rejected the null hypotheses. In other words, one-third of the studies rejected at least one of their diagnostic tests, whereas their underlying…

利用有偏代理变量评估预测表现AI 解读

This paper rehabilitates biased proxies for the assessment of the predictive accuracy of competing forecasts. By relaxing the ubiquitous assumption of proxy unbiasedness adopted in the theoretical and empirical literature, we show how to optimally combine (possibly) biased proxies to maximize the probability of inferring the ranking that would be obtained using the true latent variable, a property that we dub proxy reliability. Our procedure still preserves the robustness of…

未知异方差或工具变量下的高维二元选择模型AI 解读

This paper proposes a new method for estimating high-dimensional binary choice models. We consider a semiparametric model that places no distributional assumptions on the error term, allows for heteroskedastic errors, and permits endogenous regressors. Our approaches extend the special regressor estimator originally proposed by Lewbel (2000). This estimator becomes impractical in high-dimensional settings due to the curse of dimensionality associated with high-dimensional…

跳跃扩散过程的神经共形推断AI 解读

Bayesian inference for jump diffusion processes (JDPs) remains challenging due to intractable transition densities and the latency of jump times and intensities. This paper introduces Neural Conformal Inference for JDPs (NCoin-JDP), a novel likelihood-free approach that leverages the power of deep neural networks (DNNs). NCoin-JDP bypasses the limitations of traditional methods by establishing a direct mapping between observed data and model parameters using a DNN. This…

广义 Lee 界限AI 解读

Lee (2009) is a common approach to bound the average causal effect in the presence of selection bias, assuming the treatment effect on selection has the same sign for all subjects. This paper generalizes Lee bounds to allow the sign of this effect to be identified by pretreatment covariates, relaxing the standard (unconditional) monotonicity to its conditional analog. Asymptotic theory for generalized Lee bounds is proposed in low-dimensional smooth and high-dimensional…

具有近最优收敛率的动态空间自回归稳健估计AI 解读

Spatial autoregression has been extensively studied in various applications, yet its robust estimation methods have received limited attention. In this work, we introduce two dynamic spatial autoregression (DSAR) models aimed at capturing temporal trends and depicting the asymmetric network effects of the units. For both DSAR models, we propose a truncated Yule–Walker estimation method, which is tailored to achieve robust estimation in the presence of heavy-tailed data.…

状态变化因子模型的筛估计AI 解读

In this paper, we propose a sieve approach to estimate state-varying factor models, where the factor loadings vary over specific state variables. Our methodology consists of a two-step estimation procedure for the parameters of interest. In the first step, we achieve preliminary consistent estimates of the factors and factor loadings via nuclear norm regularization (NNR). In the second step, we perform post-NNR iterative least squares estimations for the factors and factor…

一般函数不等式检验AI 解读

This paper develops a nonparametric test for general functional inequalities that include conditional moment inequalities as a special case. It is shown that the test controls size uniformly over a large class of distributions for observed data, importantly allowing for general forms of time series dependence. New results on uniform growing dimensional Gaussian coupling for general mixingale processes are developed for this purpose, which readily accommodate most…

利用边际处理效应开展分层政策评估AI 解读

This paper proposes a unified approach to derive sharp bounds on conventional policy parameters when the instrumental variables (IVs) are potentially invalid. Using a vine copula approach, we propose a novel characterization of the identified sets for the marginal treatment effect (MTE) and the policy-relevant treatment effect (PRTE) parameters. Our method has various advantages: First, it explicitly demonstrates how imposing different IV-related assumptions with different…

因子模型中结构脉冲响应的全局识别、估计与推断：统一框架AI 解读

This paper develops a theory for the global identification, estimation and inference of impulse response functions (IRFs) in structural factor models (SFMs). We examine the impact of normalization choices on IRF identification and propose to use identification restrictions robust to such choices. A new theorem is established to address IRF identification under both recursive and nonrecursive schemes in SFMs. Moreover, we develop two new estimators for structural IRFs under…

二元结果随机实验精确置信区间的快速计算AI 解读

Given a randomized experiment with binary outcomes, exact confidence intervals for the average causal effect of the treatment can be computed through a series of permutation tests. This approach requires minimal assumptions and is valid for all sample sizes, as it does not rely on large-sample approximations such as those implied by the central limit theorem. We show that these confidence intervals can be found in O(nlogn) permutation tests in the case of balanced designs,…