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非平稳尾部依赖时间序列的时变高分位数估计AI 解读

We consider the problem of time-varying high quantile estimation for a general class of nonstationary tail dependent time series. Unlike the conventional quantile analysis in which the quantile level is typically assumed to be fixed, the current high quantile setting requires the quantile level to grow with the sample size to capture the tail phenomenon. In addition, for nonstationary time series data, the underlying data generating mechanism is expected to change over time,…

时间序列长期方差估计中的尾部后着色AI 解读

Prewhitening is a common approach to deal with strong autocorrelation. In this article, we propose a new approach called tail postcoloring, motivated by it. It uses parametric models to project, or color back, the neglected tail autocovariances in nonparametric estimators onto the final estimator. This approach bridges the non-parametric variance estimator and the parametric coloring model through a scaling factor. It automatically switches between these two arms using a…

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双参数泊松–狄利克雷隐马尔可夫模型的准共轭精确推断AI 解读

We introduce a nonparametric model for inferring time-evolving, unobserved probability distributions from discrete-time data consisting of unlabelled partitions. The latent process is a two-parameter Poisson–Dirichlet diffusion, and observations arise via exchangeable sampling. Applications include social and genetic data where only aggregate clustering summaries are observed.To address the intractable likelihood, we develop a tractable inferential framework that avoids…

稳健切片逆回归：高维重尾数据的最优估计AI 解读

Sliced inverse regression (SIR) is a flexible modeling tool that effectively reduces dimensions to reveal the complicated mechanism behind data. In recent years, SIR has been generalized to high dimensions in a variety of ways. However, all existing methods rely on the light-tailed assumption for predictors, which is frequently violated in real life. To tackle ubiquitous heavy-tailed data, we propose a novel robust SIR method, referred to as ROSE, that scales well with high…

面向政策制定者的处理效应同质性非参数检验AI 解读

Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests for both quantitative and qualitative treatment effect heterogeneity. The tests can incorporate a variety of structured assumptions on the conditional average treatment effect, allow for both continuous and discrete covariates, and do not require sample splitting to obtain a tractable asymptotic null…

竞争风险的估计与预测：贝叶斯非参数方法AI 解读

Competing risks occur in survival analysis when multiple causes of death are present. They play a prominent role in several domains extending beyond biostatistics to encompass epidemiology, actuarial sciences, and reliability theory. This paper adopts a multi–state modeling framework to competing risks. We introduce a class of flexible nonparametric priors, defined through hierarchical completely random measures, to model the transition probabilities, and identify the…

时间维度较小时异质性面板分位数回归的 CCE 估计AI 解读

We consider a common correlated effects estimator for panel quantile regression models featuring heterogeneous slope coefficients and interactive fixed effects. Using convolution smoothing to facilitate the derivation of asymptotic properties, our method allows the number of time periods (T) to grow at a much slower rate than the number of cross-sectional units (N). We establish that the mean group estimator converges to a limiting normal distribution, which may have a…

助学金与向上流动：来自哥伦比亚 Ser Pilo Paga 项目的证据AI 解读

We study the impact of financial aid on upward mobility. We estimate the causal impacts of a Colombian policy targeting high-achieving, low-socioeconomic-status students on later-life educational and labor market outcomes. Using regression discontinuity design, difference-in-differences, and population-wide administrative microdata, we find that the policy boosted attendance and completion at high-value-added colleges, particularly in STEM fields. Improved college quality…

对消费者搜索具有稳健性的离散选择模型估计方法AI 解读

We state conditions under which choice data suffice to identify preferences when consumers may not be fully informed about attributes of goods. Our approach can be used to test for full information, forecast how consumers will respond to information, and conduct welfare analysis when consumers are imperfectly informed. In a lab experiment, we successfully forecast the average response to new information when consumers engage in costly search. In data from Expedia, our method…

Cox 模型的半参数 Fiducial 推断AI 解读

Abstract R.A. Fisher introduced the fiducial distribution as a potential replacement for the Bayesian posterior distribution in the 1930s. During the past century, fiducial approaches have been explored in various parametric and nonparametric settings. However, to the best of our knowledge, no fiducial inference has been developed in the realm of semiparametric statistics. In this paper, we propose a novel fiducial approach for semiparametric models. In memory of Sir David…

时变内生随机系数面板模型的识别与估计AI 解读

This paper proposes a correlated random coefficient linear panel data model, where regressors can be correlated with time-varying and individual-specific random coefficients through both a fixed effect and a time-varying random shock. I develop a new panel data-based method to identify the average partial effect and the local average response function. The identification strategy employs a sufficient statistic to control for the fixed effect and a control variable for the…

经济与金融中的贝叶斯方法：统一综述与分类AI 解读

This special issue brings together 21 papers that reflect the expanding scope and methodological evolution of Bayesian econometrics. We introduce a new organizing taxonomy that distinguishes between Traditional Bayesian Approaches - rooted in conjugate priors, standard likelihood-based estimation, and established Markov Chain Monte Carlo (MCMC) techniques - and Contemporary Bayesian Frontiers, characterized by nonparametric methods, variational inference, high-dimensional…

双侧测量误差下的分布效应：代际收入流动应用AI 解读

This paper considers identification and estimation of distributional effect parameters that depend on the joint distribution of an outcome and another variable of interest (“treatment”) in a setting with “two-sided” measurement error—that is, where both variables are possibly measured with error. Examples of these parameters in the context of intergenerational income mobility include transition matrices, rank-rank correlations, and the poverty rate of children as a function…

多解释变量异方差回归的 AICAI 解读

The original and corrected Akaike information criteria (AIC) have been routinely used for model selection for ages. The penalty terms in these criteria are tied to the classical normal linear regression, characterized by conditional homoskedasticity and a small number of regressors relative to the sample size. We derive, from the same principles, a general version that takes account of conditional heteroskedasticity and regressor numerosity. The new AICm penalty takes a form…

基于个体特征的分位数因子模型估计AI 解读

This paper studies the estimation of characteristics-based quantile factor models where the factor loadings are unknown functions of observed individual characteristics, and the idiosyncratic error terms are subject to conditional quantile restrictions. We propose a three-stage estimation procedure that is easy to implement and has nice properties. The convergence rates, the limiting distributions of the estimated factors and loading functions, plus a consistent selection…

边界断点设计的估计与推断：基于距离的方法AI 解读

We study nonparametric distance-based (isotropic) local polynomial methods for estimating the boundary average treatment effect curve, a causal functional that captures treatment effect heterogeneity in boundary discontinuity designs. We establish identification, estimation, and inference results both pointwise and uniformly along the treatment assignment boundary. We show that the geometric regularity of the boundary, a one-dimensional manifold, plays a central role in…

未知形式异方差下非基本 ARMA 模型的时域估计AI 解读

This paper considers time domain estimation of possibly non-fundamental (that is, non-causal and/or non-invertible) non-Gaussian linear ARMA models with martingale difference innovations that may display conditional heteroskedasticity of unknown form. Instead of explicitly parametrizing the underlying volatility process (the higher order dependence) and employing maximum likelihood procedures, we propose a time domain minimum distance objective function based on innovations…

多维匹配的半非参数模型：最优传输方法AI 解读

This paper develops a set of empirically tractable and flexible sieve estimators for semi-nonparametric multidimensional matching models with transferable utility, focusing on worker-job matching. We generalize the parametric quadratic-Gaussian framework employed by Bojilov and Galichon (2016) and Lindenlaub (2017), which relies on joint normality of observed characteristics. We allow unrestricted distributions of characteristics and show identification of the production…

非参数二元选择模型的核化估计方法AI 解读

We propose a new estimator for nonparametric binary choice models that does not impose a parametric structure on either the systematic function of covariates or the distribution of the error term. A key advantage of our approach is its computational scalability in the number of covariates. For instance, even when assuming a normal error distribution as in probit models, commonly used sieves for approximating an unknown function of covariates can lead to a large-dimensional…

工具变量回归中弱工具稳健的子向量推断：拉格朗日乘子检验与 Anderson–Rubin 置信集AI 解读

We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition. This is the first weak-instrument-robust subvector test for instrumental variables regression to recover the degrees of freedom of the commonly used non-weak-instrument-robust Wald test. Additionally, we provide a closed-form solution for subvector confidence sets obtained by inverting the…

具有定向作用的工具变量与处理效应AI 解读

Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We explore the additional identifying power of a positive…

集中化 MCMC 估计AI 解读

This paper studies a concentrated Markov Chain Monte Carlo (MCMC) estimation method that offers significant computational advantages while delivering valid inference for parameters of interest. By reducing the dimension of parameters to be sampled, this method substantially decreases computational time and memory usage relative to both traditional and some state-of-the-art Bayesian algorithms. Its applicability extends to correctly specified or misspecified likelihood…

非对称风险下的宏观审慎政策：贝叶斯结构分位数 VAR 方法AI 解读

Macro-prudential policymakers face a risk-management problem: they must assess medium-term downside risks to the real economy stemming from financial imbalances and the associated threat of future financial crises, while accounting for the cost of policy interventions in terms of foregone upside potential. This trade-off between limiting downside risks and preserving upside growth is inherently asymmetric and depends on the distribution of future macroeconomic outcomes. We…

文本词项选择与分析：频率学派和贝叶斯策略及解释AI 解读

Economic analysis based on text data has expanded rapidly, yet the ultra-high dimensionality of text data presents substantial challenges for term selection and estimation. We propose the Information-Adaptive Lasso and Information-Adaptive Spike-and-Slab Lasso as novel frequentist and Bayesian approaches to address these challenges. A key theoretical contribution of this study is the establishment of the rate of convergence in term-selection consistency, which we show to be…