论文检索

基于重复测量的反卷积估计量的一致收敛AI 解读

This paper studies the uniform convergence rates of Li and Vuong’s (1998, Journal of Multivariate Analysis 65, 139–165; hereafter LV) nonparametric deconvolution estimator and its regularized version by Comte and Kappus (2015, Journal of Multivariate Analysis 140, 31–46) for the classical measurement error model, where repeated noisy measurements on the error-free variable of interest are available. In contrast to LV, our assumptions allow unbounded supports for the…

非理性亢奋：校正概率估计中的偏差AI 解读

We consider the common setting where one observes probability estimates for a large number of events, such as default risks for numerous bonds. Unfortunately, even with unbiased estimates, selecting events corresponding to the most extreme probabilities can result in systematically underestimating the true level of uncertainty. We develop an empirical Bayes approach “excess certainty adjusted probabilities” (ECAP), using a variant of Tweedie’s formula, which updates…

存在测量误差的间歇性食物摄入分布的半参数估计AI 解读

Dietary data collected from 24-hour dietary recalls are observed with significant measurement errors. In the nonparametric curve estimation literature, much of the effort has been devoted to designing methods that are consistent under contamination by noise, and which have been traditionally applied for analyzing those data. However, some foods such as alcohol or fruits are consumed only episodically, and may not be consumed during the day when the 24-hour recall is…

结合 AdaBoost 的多类别半参数模型平均预测AI 解读

Model average techniques are very useful for model-based prediction. However, most earlier works in this field focused on parametric models and continuous responses. In this article, we study varying coefficient multinomial logistic models and propose a semiparametric model averaging prediction (SMAP) approach for multi-category outcomes. The proposed procedure does not need any artificial specification of the index variable in the adopted varying coefficient sub-model…

平滑样条半参数密度模型AI 解读

Jiahui Yua, Jian Shib, Anna Liuc & Yuedong Wangd*a Department of Mathematics and Statistics, Boston University, Boston, MAb PayPal, San Jose, CAc Department of Mathematics and Statistics, University of Massachusetts, Amherst, MAd Department Statistics and Applied Probability, University of California, Santa Barbara, CAColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are…

分组回归与稀疏广义加性模型的尖峰—厚尾组 LassoAI 解读

–We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse generalized additive models (GAMs), thereby introducing the first nonparametric variant of the spike-and-slab lasso methodology. Our model simultaneously performs group selection and estimation, while our fully Bayes treatment of the mixture proportion allows for model complexity control and…

函数型线性回归：序列依赖与误差污染AI 解读

Functional linear regression is an important topic in functional data analysis. It is commonly assumed that samples of the functional predictor are independent realizations of an underlying stochastic process, and are observed over a grid of points contaminated by iid measurement errors. In practice, however, the dynamical dependence across different curves may exist and the parametric assumption on the error covariance structure could be unrealistic. In this article, we…

半参数因子模型中的同质性与结构识别AI 解读

Factor modeling is an essential tool for exploring intrinsic dependence structures in financial and economic studies through the construction of common latent variables, including the famous Fama–French three factor models for the description of asset returns in finance. However, most of the existing statistical methods for analyzing latent factors have been developed through a linear approach. In this article, we consider a semiparametric factor model and present a…

股票收益的非线性可预测性？预测回归中的参数与非参数推断AI 解读

Nonparametric test procedures in predictive regressions have χ2 limiting null distributions under both low and high regressor persistence, but low local power compared to misspecified linear predictive regressions. We argue that IV inference is better suited (in terms of local power) for analyzing additive predictive models with uncertain predictor persistence. Then, a two-step procedure is proposed for out-of-sample predictions. For the current estimation window, one first…

因子数增长时充分预测的非参数估计与共形推断AI 解读

The sufficient forecasting (SF) provides a nonparametric procedure to estimate forecasting indices from high-dimensional predictors to forecast a single time series, allowing for the possibly nonlinear forecasting function. This article studies the asymptotic theory of the SF with a diverging number of factors and develops its predictive inference. First, we revisit the SF and explore its connections to Fama–MacBeth regression and partial least squares. Second, with a…

高维数据下条件平均处理效应的估计AI 解读

Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the…

反事实处理效应：估计与推断AI 解读

This article proposes statistical methods to evaluate the quantile counterfactual treatment effect (QCTE) if one were to change the composition of the population targeted by a status quo program. QCTE enables a researcher to carry out an ex-ante assessment of the distributional impact of certain policy interventions or to investigate the possible explanations for treatment effect heterogeneity. Assuming unconfoundedness and invariance of the conditional distributions of the…

均值结构与自相关稳健的协方差矩阵估计AI 解读

We consider estimation of the asymptotic covariance matrix in nonstationary time series. A nonparametric estimator that is robust against unknown forms of trends and possibly a divergent number of change points (CPs) is proposed. It is algorithmically fast because neither a search for CPs, estimation of trends, nor cross-validation is required. Together with our proposed automatic optimal bandwidth selector, the resulting estimator is both statistically and computationally…

非参数样本选择下删失回归模型的半参数估计AI 解读

This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normality of the proposed estimator are established under mild regularity conditions. A Monte Carlo simulation study indicates that the estimator performs well in various designs and outperforms parametric maximum likelihood estimators. An empirical application to female smoking is…

非参数非经典测量误差方法下的代际流动弹性估计AI 解读

This article provides a framework for estimating intergenerational mobility elasticities (IGEs) of children’s income with respect to parental income. We allow the IGEs to be heterogeneous, by leaving the relationship of parental and child incomes unspecified, while acknowledging and addressing the latent nature of both child and parental permanent incomes and the resulted life-cycle bias. Our framework enables us to test the widely imposed assumption that the…

利用多幂变差估计跳跃活跃度AI 解读

Realized multipower variation, originally introduced to eliminate jumps, can be extremely useful for inference in pure-jump models. This article shows how to build a simple and precise estimator of the jump activity index of a semimartingale observed at a high frequency by comparing different multipowers. The novel methodology allows to infer whether a discretely observed process contains a continuous martingale component. The empirical part of the article undertakes a…

半参数尾指数回归AI 解读

Abstract–Understanding why extreme events occur is often of major scientific interest in many fields. The occurrence of these events naturally depends on explanatory variables, but there is a severe lack of flexible models with asymptotic theory for understanding this dependence, especially when variables can affect the outcome nonlinearly. This article proposes a novel semiparametric tail index regression model to fill the gap for this purpose. We construct consistent…

异方差分数单整时间序列模型中的自适应推断AI 解读

We consider estimation and inference in fractionally integrated time series models driven by shocks which can display conditional and unconditional heteroscedasticity of unknown form. Although the standard conditional sum-of-squares (CSS) estimator remains consistent and asymptotically normal in such cases, unconditional heteroscedasticity inflates its variance matrix by a scalar quantity, λ>1, thereby inducing a loss in efficiency relative to the unconditionally…

符号正则核的低秩逼近AI 解读

Parameterized integrals, q y = ∫ K x , y d P x , are common in economic applications. To optimize a parameterized integral, or to relate one such integral to others, it is helpful to reduce the rank of the kernel K while controlling approximation error. A bound on the uniform approximation error of K x , y ≈ ∑ i = 1 n f i x g i y also bounds the uniform approximation error of q y ≈ ∑ i = 1 n c i g i y = ∑ i = 1 n ∫ f i x d P x g i y over all y and all signed measures P whose…

平均线性回归函数的半参数有效估计AI 解读

Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, or non-mutually exclusive “treatments”. Consider the linear regression of Y onto X in a subpopulation homogeneous in W=w (formally a conditional linear predictor). Let b0w be the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average β0=Eb0W.…

利用多来源数据估计与推断半参数模型AI 解读

This paper studies the estimation and inference of nonlinear econometric models when the economic variables are contained in different data sets. We construct a semiparametric minimum distance (SMD) estimator of the unknown structural parameter of interest when there are some common conditioning variables in different data sets. The SMD estimator is shown to be consistent and has an asymptotic normal distribution. We provide the explicit form of the optimal weight for the…

国家分裂的经济学：评估分离独立的经济影响AI 解读

Abstract This paper presents estimates of the economic effects of secession for a large panel of countries that gained independence between 1940 and 2016. It relies on a semi‐parametric identification strategy that controls for the confounding effects of past gross domestic product (GDP) dynamics, anticipation effects, unobserved heterogeneity, model uncertainty, and effect heterogeneity. Our findings indicate that secession lowered per capita GDP, on average by around 24%…

空间相关稳健推断AI 解读

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a…

局部稳健的半参数估计AI 解读

Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification…