论文检索

多资产跨期消费的分位数方法AI 解读

This paper develops a novel economic model and econometric methods to jointly identify and estimate parameters related to intertemporal preference and risk attitude. We begin by formulating an intertemporal consumption model with multiple assets based on dynamic quantile preferences that account for elasticity of intertemporal substitution, risk attitude, and discount factor. We establish the properties of the model and obtain interesting explicit expressions for the value…

局部平稳条件下时变因子模型的推断AI 解读

This paper considers estimation of and testing for a class of locally stationary time series factor models with evolutionary dynamics, where the entries and dimension of the factor loading matrix are allowed to vary with time while the factors and idiosyncratic components are locally stationary. We propose an adaptive sieve estimator for the span of the time-varying loading matrix of a locally stationary factor process. A uniformly consistent estimator of the effective…

动态面板门槛模型的自助法推断AI 解读

This paper develops valid bootstrap inference methods for the dynamic short panel threshold regression. We show that the standard nonparametric bootstrap is inconsistent for the first-differenced generalized method of moments (GMM) estimator. The inconsistency arises from an n1/4-consistent non-normal asymptotic distribution of the threshold estimator when the true parameter lies in the continuity region of the parameter space, which stems from the rank deficiency of the…

存在样本构成变化的双重差分AI 解读

This paper studies Difference-in-Differences (DiD) setups with repeated cross-sectional data and potential compositional changes across time periods. We begin our analysis by deriving the efficient influence function and the semiparametric efficiency bound for the average treatment effect on the treated (ATT). We introduce nonparametric estimators that attain the semiparametric efficiency bound under mild rate conditions on the estimators of the nuisance functions,…

系统性风险驱动投资组合选择的统计推断AI 解读

Portfolio selection in modern finance involves constructing optimal asset allocation strategies that balance risk and return. However, traditional portfolio selection faces new challenges due to systemic events, as exemplified by the financial crisis and the COVID-19 pandemic. In response, we introduce a nonparametric systemic risk-driven portfolio selection approach that models market and portfolio losses using kernel density estimation. In the event of market…

非平稳与周期时间序列的函数型半参数建模AI 解读

Inspired by a real data example illustrating the periodicity in hog price data, this study aims to analyze time series that exhibit an unknown period alongside complex covariate effects. To address these complexities and effectively handle the data structures, we incorporate the partial functional varying-coefficient single-index model into the classical time series decomposition model. We propose a two-stage estimation procedure designed to accurately estimate the unknown…

结合控制函数的准贝叶斯估计与推断AI 解读

This paper introduces a quasi-Bayesian method that integrates frequentist nonparametric estimation with Bayesian inference in a two-stage process. Applied to an endogenous discrete choice model, the approach first uses kernel or sieve estimators to estimate the control function nonparametrically, followed by Bayesian methods to estimate the structural parameters. This combination leverages the advantages of both frequentist tractability for nonparametric estimation and…

局部稳健且半参数有效的贝叶斯推断AI 解读

We propose a framework for making Bayesian parametric models robust to local misspecification. Suppose in a baseline parametric model, a parameter of interest has an interpretation in an encompassing semiparametric model. Bayesian and maximum likelihood estimators are generally biased under local misspecification. We propose to augment the baseline likelihood by a multiplicative factor that involves scores for the baseline model, the efficient scores for the encompassing…

含噪协变量的因果推断：异方差测量误差与差分隐私AI 解读

We study causal inference with high‐dimensional covariates, observed with various types of heteroscedastic noise. Our main assumption is that the true covariates are low rank, which we empirically evaluate with plots and interpret as approximate repeated measurements. This assumption delivers semiparametric inference on the causal parameter, as precisely as if the true covariates were available. A methodological contribution is to introduce an error‐in‐variable balancing…

数据丰富环境中非对称损失下的二元选择：理论与算法公平性应用AI 解读

We study the binary choice problem in a data‐rich environment with asymmetric loss functions. The econometrics literature covers nonparametric binary choice problems but does not offer computationally attractive solutions in data‐rich environments. The machine learning literature has many algorithms but is focused mostly on loss functions that are independent of covariates. We show that theoretically valid decisions on binary outcomes with general loss functions can be…

网络干扰下因果推断的局部方法AI 解读

We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, disease and financial contagion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other…

样本选择下的分布回归与英国工资分解AI 解读

We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model, which accommodates much richer patterns of heterogeneity in the effect of covariates and selection process and allows for departures from Gaussian errors while maintaining the same level of tractability. The model applies to continuous, discrete, and mixed outcomes. We provide identification, estimation, and inference…

滚动验证下的在线估计：流式数据的自适应非参数估计AI 解读

Online nonparametric estimators are gaining popularity due to their efficient computation and competitive generalization abilities. An important example includes variants of stochastic gradient descent. These algorithms often take one sample point at a time and incrementally update the parameter estimate of interest. In this work, we consider model selection/hyperparameter tuning for such online algorithms. We propose a weighted rolling validation procedure, an online…

核岭回归的几何分析及其应用AI 解读

We obtain upper bounds for the estimation error of Kernel Ridge Regression (KRR) for all nonnegative regularization parameters, offering a geometric perspective on various phenomena in KRR. As applications: 1. We address the Multiple Descents problem, unifying the proofs of (In Proceedings of Thirty Third Conference on Learning Theory (2020) 2683–2711 PMLR) and (Ann. Statist. 49 (2021) 1029–1054) for polynomial kernels in nonasymptotic regime and we establish Multiple…

灵活应对赢家诅咒的推断方法AI 解读

Across science and policy, decision-makers often need to draw conclusions about the best candidate among competing alternatives. For instance, researchers may seek to infer the effectiveness of the most successful treatment or determine which demographic group benefits most from a specific treatment. Similarly, in machine learning, practitioners are often interested in the population performance of the model that performs best empirically. However, cherry-picking the best…

非参数隐马尔可夫与独立同分布模型中的聚类风险AI 解读

We conduct an in-depth analysis of the Bayes risk of clustering in the context of hidden Markov and i.i.d. models. In both settings, we identify the situations where this risk is comparable to the Bayes risk of classification and those where its minimizer, the Bayes clusterer, can be derived from the Bayes classifier. While we demonstrate that clustering based on the Bayes classifier does not always match the optimal Bayes clusterer, we show that this difference is primarily…

序贯实验中的反事实推断AI 解读

We consider after-study statistical inference for sequentially designed experiments wherein multiple units are assigned treatments for multiple time points using treatment policies that adapt over time. Our goal is to provide inference guarantees for the counterfactual mean at the smallest possible scale—mean outcome under different treatments for each unit and each time—with minimal assumptions on the adaptive treatment policy. Without any structural assumptions on the…

通过协变量调整消除因果目标选择偏差的共同原因原则AI 解读

Average treatment effects (ATEs) may be subject to selection bias when they are estimated among only a nonrepresentative subset of the target population. Selection bias can sometimes be eliminated by conditioning on a “sufficient adjustment set” of covariates, even for some forms of missingness not at random (MNAR). Without requiring full specification of the causal structure, we consider sufficient adjustment sets to allow nonparametric identification of conditional ATEs in…

完备性是否必要？未识别线性模型中的估计AI 解读

Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the nonsingularity of a high-dimensional matrix or operator and may fail for finite samples or even at the population level. Regularized estimators provide a…

可能包含大量协变量的断点回归设计AI 解读

This article examines high-dimensional covariates in regression discontinuity design (RDD) analysis. We introduce estimation and inference methods for the RDD models that incorporate covariate selection while maintaining stability across various numbers of covariates. The proposed methods combine a localization approach using kernel weights with $\ell _{1}$ -penalization to handle high-dimensional covariates. We provide both theoretical and numerical evidence demonstrating…

单调样本流失下的有效估计AI 解读

Attrition is monotonic when agents leaving multi-period studies do not return. Under a general missing at random (MAR) assumption, we study efficiency in estimation of parameters defined by moment restrictions on the distributions of the counterfactuals that were unrealized due to monotonic attrition. We discuss novel issues related to overidentification, usability of sample units, and the information content of various MAR assumptions for estimation of such parameters. We…

通过边际处理效应进行福利分析AI 解读

We consider a causal structure with endogeneity, i.e., unobserved confoundedness, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization rules. Focusing…

连续主层的半参数局部化主分层分析AI 解读

Abstract Principal stratification is essential for revealing causal mechanisms involving post-treatment intermediate variables, in real-world applications like surrogate marker evaluation. Principal stratification analysis with continuous intermediate variables is increasingly common but challenging due to the infinite principal strata and the nonidentifiability and nonregularity of principal causal effects (PCEs). Inspired by recent research, we resolve these challenges by…

部分放宽正交区组结构下混合水平多层析因设计的正交化矩偏差AI 解读

Abstract Multi-stratum factorial designs, such as block designs and row–column designs, are widely used for screening treatment factors in experiments involving complex structures of experimental units due to multiple sources of error. In this study, we propose a unified model-free approach, termed orthogonalized moment aberration, to compare the similarities between level combinations of treatment factors assigned to heterogeneous experimental units. The proposed approach,…