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利用南极冰芯数据评估温度与冰积累速率的半参数模型AI 解读

In this paper, we present a semiparametric model for describing the effect of temperature on Antarctic ice accumulation on a paleoclimatic time scale. The model is motivated by sharp ups and downs in the rate of ice accumulation apparent from ice core data records, which are synchronous with movements of temperature. We prove consistency of the estimators under reasonable conditions. We conduct extensive simulations to assess the performance of the estimators and their…

GS-BART：采用图分裂决策规则的贝叶斯加性回归树AI 解读

Ensemble decision tree methods such as XGBoost, Random Forest, and Bayesian Additive Regression Trees (BART) have gained enormous popularity in data science for their superior performance in machine learning regression and classification tasks. In this paper, we introduce a new Bayesian graph-split additive decision tree method, GS-BART, designed to enhance the performance of axis-parallel split-based BART for dependent data with graph structures. The proposed approach…

高维暴露与高维中介模型的统计推断AI 解读

High-dimensional mediation analysis has gained increasing interest in various fields, particularly in genetic and medical research. Compared with existing works that focus mainly on high-dimensional mediators, this paper advocates a new framework of Partial Regularization-based Inference for Mediation Effects (PRIME) when both exposures and mediators are high-dimensional. Estimated direct and indirect effects are established using a group-wise partially penalized least…

通过变换复合构建回归AI 解读

We describe a modular regression framework in which covariate-dependent transformations are composed together and act on probability distributions. This framework is based on group actions of vector spaces, which are computationally convenient families of transformations that are well suited to model building and inference. We quantify covariate contributions to each group action through corresponding linear maps, and these are the only model parameters to be estimated.…

Summary We adapt double machine learning to macroeconomic time series by combining regularized nuisance estimation with reverse cross-fitting. This deterministic scheme exploits time reversibility to use time-reversed auxiliary blocks and, unlike neighbour-deletion designs, avoids buffer blocks, thereby improving sample usage. We derive conditions for asymptotic validity and show in simulations that the estimator performs well in realistic finite samples across the designs…

自适应实验的设计稳定性及其对处理效应估计的影响AI 解读

Abstract We study the problem of estimating the average treatment effect under sequentially adaptive treatment assignment mechanisms. In contrast to classical completely randomized designs, the setting we consider is one in which the probability of assigning treatment to each experimental unit may depend on prior assignments and observed outcomes. Within the potential outcomes framework (Neyman, 1923), we propose and analyse two natural estimators for the average treatment…

基于球散度的条件独立性度量与局部野自助检验AI 解读

Abstract In this paper we introduce a new measure of conditional dependence between two random vectors $ {\boldsymbol{X}} $ and $ {\boldsymbol{Y}} $, given another random vector $ \boldsymbol{Z} $ using the ball divergence. Our measure characterizes conditional independence and does not require any moment assumptions. We propose an estimator of the measure using a kernel-averaging technique and derive its asymptotic distribution. Using this estimator, we construct a test for…

随机试验中 Rosenbaum 秩估计量的稳健性与效率：设计视角AI 解读

Summary Mean-based estimators of causal effects in randomized experiments may behave poorly if the potential outcomes have a heavy tail or contain outliers. An alternative estimator proposed by Rosenbaum (1993) estimates a constant additive treatment effect by inverting a randomization test using ranks. We develop a design-based asymptotic theory for this rank-based estimator and study its robustness and efficiency properties. We show that Rosenbaum’s estimator is robust…

资产价格能否帮助预测通胀？来自个股价格的证据AI 解读

This paper revisits the predictive power of asset prices for inflation, focusing on individual stock prices rather than aggregate indices. Using a large panel data of firm-level stock prices and applying machine learning techniques, we demonstrate that individual stock prices significantly enhance the accuracy of inflation forecasts, particularly over medium- to long-term horizons and during periods of high inflationary and deflationary pressure. Compared to composite and…

平稳时间序列自相关的稳健置信区间AI 解读

This paper develops a simple and new approach for testing linear hypotheses about autocorrelations for time series with general stationary serial correlation structures. A practically important special case is the computation of robust confidence intervals for individual autocorrelations that do not require resampling methods. Inference is heteroskedasticity and autocorrelation robust (HAR) and allows innovations to be uncorrelated but not necessarily independent and…

基于误差协方差特征分解的快速、顺序不变 VAR 贝叶斯推断AI 解读

Bayesian inference in Vector Autoregressions (VARs) involves manipulating large matrices which appear in the posterior (or conditional posterior) of the VAR coefficients. For large VARs, the computational burden of these manipulations can render empirical work impractical. In response to this, many researchers transform their VARs so as to allow for Bayesian estimation to proceed one equation at a time. This leads to a massive reduction in the computational burden. This…

空间干扰效应的高维检测AI 解读

Modeling interference effects in high-dimensional settings presents significant challenges due to the complexity of underlying dependence structures. Existing approaches often rely on explicit and homogeneous assumptions, limiting their applicability in real-world scenarios. In this paper, we introduce a novel low-rank and sparse treatment effect model that leverages high-dimensional techniques to identify interference structures without restrictive parametric assumptions.…

分位数中的区制特定股票收益可预测性AI 解读

This article captures the underlying regime switching mechanism in the prediction of stock returns via a predictive quantile regression with multiple thresholds. The predictability of each predictor is allowed to switch from one regime to another according to the value of a threshold variable and could vary across quantiles, and the predictors could possess heterogeneous degrees of persistence. Statistical learning techniques, including a sequential estimation procedure, an…

时变面板数据模型中的潜在子群识别AI 解读

This article introduces a time-varying panel data model that incorporates latent group structures, designed to tackle both individual heterogeneity and smooth structural changes over time. We develop an innovative center-augmented K-power means (KPM) methodology that promotes convergence of subjects toward their respective cluster centers, enabling the identification of latent group structures without requiring prior knowledge of group composition. This approach delivers…

含异常值高维数据的稳健因子分析AI 解读

Outliers are commonly encountered in financial and economic data, and pose significant challenges to the study of the conventional factor models. This article focuses on factor analysis for high-dimensional data in the presence of random outliers. We first propose a new class of factor model which incorporates a sparse outlier matrix. We then develop a class of robust methods to estimate the factor matrix and the number of factors. Under mild conditions, consistency of the…

动态空间自回归模型的推断与变点检测AI 解读

We analyze a varying-coefficient spatial autoregressive model with spatial fixed effects. One salient feature of the model is the incorporation of multiple spatial weight matrices through their linear combinations with varying coefficients, which help solve the problem of choosing the most “correct” one for applied econometricians who often face the availability of multiple expert spatial weight matrices. We estimate and make inferences on the model coefficients and…

交互固定效应面板中潜在组结构的收缩估计与识别AI 解读

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…

条件分位数处理效应估计的深度正交学习器AI 解读

In recent years, orthogonal statistical learning has been widely recognized for its ability to reduce sensitivity with respect to nuisance parameters to estimate the target parameter, making it an important tool in causal inference, particularly in the estimation of the conditional average treatment effect (CATE). However, its application on conditional quantile treatment effect (CQTE), which offers a more comprehensive perspective on treatment effects than CATE, has not yet…

含内生性的时变线性变换面板模型中的偏效应AI 解读

This article develops a new estimator for an average partial effect in nonlinear panel models, where outcomes are time-varying monotonic transformations of latent variables that include fixed effects and endogenous regressors. The partial effect can be time-varying and the counterfactual shift is scale invariant—key advantages over the linear model. We exploit a conditional moment restriction and address the ill-posed nature of recovering the transformation functions using…

将预测能力推断扩展至机器学习方法AI 解读

The use of machine learning methods for predictive purposes has exponentially increased over the past two decades, but uncertainty quantification for predictive comparisons remains challenging and underdeveloped. This article addresses this gap by extending the classic inference theory for predictive ability in time series to modern machine learners, such as Lasso or Deep Learning. We investigate under which conditions such extensions are possible. For standard out-of-sample…

二元结果与线性交互AI 解读

We consider general models of linear interactions in binary outcomes. We first characterize the conditions under which these models are coherent and complete. Next, we show that models of linear interactions admit sound microfoundations under assumptions of incomplete information and independence. To help guide practitioners, we discuss key modeling choices and testable restrictions. We then consider a specification that is linear in parameters, and propose a simple…

一般未知因子下共同相关效应回归的横截面自助法AI 解读

The Common Correlated Effects (CCE) approach enjoys considerable popularity for estimating factor-augmented panel data models. A key benefit is that by orthogonalizing the data on the available cross-section averages, the unobserved components are eliminated from the data, regardless of their order(s) of integration. This obviates the need for such knowledge, and makes CCE particularly attractive for macroeconomic applications, where the set of unobservables might contain…

极值蒙特卡洛滤波器：通过模拟与回归提取信号AI 解读

We introduce a novel simulation-based method for signal extraction in a general class of state space models. It can be used to estimate time-varying conditional means, modes, and quantiles, and to predict latent variables or forecast observations. The method consists of generating artificial datasets from the model and estimating the quantities of interest via extremum estimation. The approach is broadly applicable and its implementation is straightforward. The method is…

含影响节点与社群的高维分位数向量自回归AI 解读

Quantile vector autoregression (QVAR) models offer enhanced capabilities over vector autoregression (VAR) models in analyzing asymmetric interactions within multiple time series and are widely used in many areas, including finance and economics. However, in scenarios involving high-dimensional data where the number of time series N exceeds the length of the time series T, both parameter estimation and the theoretical establishment present significant challenges. Notably,…