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非参数工具变量回归的最优上确界范数收敛率与非线性泛函一致推断AI 解读

This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h0 and functionals of h0. First, we derive sup‐norm convergence rates for computationally simple sieve NPIV (series two‐stage least squares) estimators of h0 and its derivatives. Second, we derive a lower bound that describes the best possible (minimax) sup‐norm rates of estimating h0 and its derivatives, and…

尖锐断点回归估计量的两侧最优带宽联合选择AI 解读

A new bandwidth selection method that uses different bandwidths for the local linear regression estimators on the left and the right of the cut‐off point is proposed for the sharp regression discontinuity design estimator of the average treatment effect at the cut‐off point. The asymptotic mean squared error of the estimator using the proposed bandwidth selection method is shown to be smaller than other bandwidth selection methods proposed in the literature. The approach…

相依与受限观测下不连续准则的局部M估计AI 解读

We examine the asymptotic properties of local M-estimators under three sets of high-level conditions. These conditions are sufficiently general to cover the minimum volume predictive region, the conditional maximum score estimator for a panel data discrete choice model and many other widely used estimators in statistics and econometrics. Specifically, they allow for discontinuous criterion functions of weakly dependent observations which may be localized by kernel smoothing…

We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η0, we consider the use of statistical or machine learning (ML) methods, which are particularly…

单侧检验中控制错误发现率的预知与自适应方法：理论及处理效应评价应用AI 解读

Economists are often interested in identifying effective policies or treatments together with subpopulations of individuals who respond positively (or with a sign that is expected) to these treatment interventions. In this paper, we propose an optimal false discovery rate controlling method that is especially useful for such one‐sided testing problems. The proposed procedure is optimal in the sense of minimizing the false non‐discovery rate while controlling the false…

短期删失动态面板模型的半参数识别与估计AI 解读

In this paper, we present a semi‐parametric identification and estimation method for censored dynamic panel data models of short time periods and their average partial effects with only two periods of data. The proposed method transforms the semi‐parametric specification of censored dynamic panel data models into a parametric family of distribution functions of observables without specifying the distribution of the initial condition. Then the censored dynamic panel data…

严格外生工具变量下线性回归的简便稳健估计AI 解读

In this paper, I investigate the estimation of linear regression models with strictly exogenous instruments under minimal identifying assumptions. I introduce a uniformly (in the data‐generating process) consistent estimator under nearly minimal identifying assumptions. The proposed estimator, called the integrated instrumental variables (IIV) estimator, is a simple weighted least‐squares estimator. It does not require the choice of a bandwidth or tuning parameter, or the…

具有局部平稳回归变量的非参数固定效应面板模型AI 解读

We develop methods for inference in nonparametric time-varying fixed effects panel data models that allow for locally stationary regressors and for the time series length T and cross-section size N both being large. We first develop a pooled nonparametric profile least squares dummy variable approach to estimate the nonparametric function, and establish the optimal convergence rate and asymptotic normality of the resultant estimator. We then propose a test statistic to check…

未知连接函数广义可加模型的有效估计与计算AI 解读

The generalised additive models (GAM) are widely used in data analysis. In the application of the GAM, the link function involved is usually assumed to be a commonly used one without justification. Motivated by a real data example with binary response where the commonly used link function does not work, we propose a generalised additive models with unknown link function (GAMUL) for various types of data, including binary, continuous and ordinal. The proposed estimators are…

面板模型平滑结构变化的非参数检验AI 解读

Detecting and modeling structural changes in time series models have attracted great attention. However, relatively little effort has been paid to the testing of structural changes in panel data models despite their increasing importance in economics and finance. In this paper, we propose a new approach to testing structural changes in panel data models. Unlike the bulk of the literature on structural changes, which focuses on detection of abrupt structural changes, we…

内生样本选择下的非参数估计AI 解读

This paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection conditionally on potential outcomes. Endogeneity of regressors is also allowed for. In the exogenous and endogenous case, consistent two-step estimation procedures are proposed and their rates of convergence are derived. Pointwise asymptotic distribution of the…

对称性限制下样本选择模型的非参数识别与估计AI 解读

Under a conditional mean restriction Das et al. (2003) considered nonparametric estimation of sample selection models. However, their method can only identify the outcome regression function up to a constant. In this paper we strengthen the conditional mean restriction to a symmetry restriction under which selection biases due to selection on unobservables can be eliminated through proper matching of propensity scores; consequently we are able to identify and obtain…

条件矩不等式的自适应检验AI 解读

Many economic models yield conditional moment inequalities that can be used for inference on parameters of these models. In this paper, I construct new tests of parameter hypotheses in conditional moment inequality models based on studentized kernel estimates of moment functions. The tests automatically adapt to the unknown smoothness of the moment functions, have uniformly correct asymptotic size, and are rate-optimal against certain classes of alternatives. Some existing…

相依因子模型中联合分布的识别AI 解读

This paper studies linear factor models that have arbitrarily dependent factors. Assuming that the coefficients are known and that their matrix representation satisfies rank conditions, we identify the nonparametric joint distribution of the unobserved factors using first and then second-order partial derivatives of the log characteristic function of the observed variables. In conjunction with these identification strategies the mean and variance of the vector of factors are…

异方差误差删失线性回归模型的半参数效率AI 解读

Using a simplified approach developed by Severini and Tripathi (2001), we calculate the semiparametric efficiency bound for the finite-dimensional parameters of censored linear regression models with heteroskedastic errors. Under an additional identification at infinity type assumption, we propose an efficient estimator based on a novel result from Lewbel and Linton (2002). An extension to censored partially linear single-index models is also presented.

基于极值理论的条件风险价值与条件预期短缺非参数估计AI 解读

We propose nonparametric estimators for conditional value-at-risk (CVaR) and conditional expected shortfall (CES) associated with conditional distributions of a series of returns on a financial asset. The return series and the conditioning covariates, which may include lagged returns and other exogenous variables, are assumed to be strong mixing and follow a nonparametric conditional location-scale model. First stage nonparametric estimators for location and scale are…

局部隐私估计的极小极大最优程序AI 解读

Working under a model of privacy in which data remain private even from the statistician, we study the tradeoff between privacy guarantees and the risk of the resulting statistical estimators. We develop private versions of classical information-theoretical bounds, in particular those due to Le Cam, Fano, and Assouad. These inequalities allow for a precise characterization of statistical rates under local privacy constraints and the development of provably (minimax) optimal…

BNP-Seq：测序计数数据的贝叶斯非参数差异表达分析AI 解读

We perform differential expression analysis of high-throughput sequencing count data under a Bayesian nonparametric framework, removing sophisticated ad hoc pre-processing steps commonly required in existing algorithms. We propose to use the gamma (beta) negative binomial process, which takes into account different sequencing depths using sample-specific negative binomial probability (dispersion) parameters, to detect differentially expressed genes by comparing the posterior…

动态有序回归关系建模：加州岩鱼成熟度估计AI 解读

We develop a Bayesian nonparametric framework for modeling ordinal regression relationships, which evolve in discrete time. The motivating application involves a key problem in fisheries research on estimating dynamically evolving relationships between age, length, and maturity, the latter recorded on an ordinal scale. The methodology builds from nonparametric mixture modeling for the joint stochastic mechanism of covariates and latent continuous responses. This approach…

事件发生时间测量误差的非参数校正：风险预测模型应用AI 解读

Mismeasured time to event data used as a predictor in risk prediction models will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event predictor often measured with error, used in Mendelian risk prediction models. Using validation data, we propose a method to adjust for this type of error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to…

物种发现的多臂老虎机：贝叶斯非参数方法AI 解读

Marco Battistona, Stefano Favarob* & Yee Whye Tehca Department of Decision Sciences, Bocconi University, Italyb Department of Economics and Statistics, University of Torino and Collegio Carlo Alberto, Torino, Italyc Department of Statistics, University of Oxford, Oxford, United KingdomCONTACT Stefano Favaro stefano.favaro@unito.it Department of Economics and Statistics, University of Torino, 10124 Torino, Italy and Collegio Carlo Alberto, 10024 Moncalieri TO, ItalyColor…

贝叶斯半参数多元密度反卷积AI 解读

We consider the problem of multivariate density deconvolution when interest lies in estimating the distribution of a vector valued random variable X but precise measurements on X are not available, observations being contaminated by measurement errors U. The existing sparse literature on the problem assumes the density of the measurement errors to be completely known. We propose robust Bayesian semiparametric multivariate deconvolution approaches when the measurement error…

相关随机测度AI 解读

We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian nonparametric models. Hierarchical Bayesian nonparametric models are usually built from completely random measures, a Poisson-process-based construction in which the atom weights are independent. Completely random measures imply strong independence assumptions in the corresponding hierarchical…

伴随终末事件的纵向数据条件建模AI 解读

We consider a random effects model for longitudinal data with the occurrence of an informative terminal event that is subject to right censoring. Existing methods for analyzing such data include the joint modeling approach using latent frailty and the marginal estimating equation approach using inverse probability weighting; in both cases the effect of the terminal event on the response variable is not explicit and thus not easily interpreted. In contrast, we treat the…