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稀疏高维模型下的分布式检验与估计AI 解读

This paper studies hypothesis testing and parameter estimation in the context of the divide-and-conquer algorithm. In a unified likelihood-based framework, we propose new test statistics and point estimators obtained by aggregating various statistics from $k$ subsamples of size $n/k$, where $n$ is the sample size. In both low dimensional and sparse high dimensional settings, we address the important question of how large $k$ can be, as $n$ grows large, such that the loss of…

单调函数的中等偏差与非参数推断AI 解读

This paper considers self-normalized limits and moderate deviations of nonparametric maximum likelihood estimators for monotone functions. We obtain their self-normalized Cramér-type moderate deviations and limit distribution theorems for the nonparametric maximum likelihood estimator in the current status model and the Grenander-type estimator. As applications of the results, we present a new procedure to construct asymptotical confidence intervals and asymptotical…

高维主成分分析中 AIC 与 BIC 估计显著成分数的一致性AI 解读

In this paper, we study the problem of estimating the number of significant components in principal component analysis (PCA), which corresponds to the number of dominant eigenvalues of the covariance matrix of $p$ variables. Our purpose is to examine the consistency of the estimation criteria AIC and BIC based on the model selection criteria by Akaike [In 2nd International Symposium on Information Theory (1973) 267–281, Akadémia Kiado] and Schwarz [Estimating the dimension…

利用重新拟合交叉验证方差估计检验高维回归系数AI 解读

Testing a hypothesis for high-dimensional regression coefficients is of fundamental importance in the statistical theory and applications. In this paper, we develop a new test for the overall significance of coefficients in high-dimensional linear regression models based on an estimated U-statistics of order two. With the aid of the martingale central limit theorem, we prove that the asymptotic distributions of the proposed test are normal under two different distribution…

最优动态治疗方案的高维A-learningAI 解读

Precision medicine is a medical paradigm that focuses on finding the most effective treatment decision based on individual patient information. For many complex diseases, such as cancer, treatment decisions need to be tailored over time according to patients' responses to previous treatments. Such an adaptive strategy is referred as a dynamic treatment regime. A major challenge in deriving an optimal dynamic treatment regime arises when an extraordinary large number of…

波动率变化检验AI 解读

In this paper, we propose a consistent U‐statistic test with good sampling properties to detect changes in volatility. We show that the test has a limiting standard normal distribution under the null hypothesis, and that it is powerful compared with various alternatives. A Monte Carlo experiment is conducted to highlight the merits of the proposed test relative to other popular tests for structural changes in volatility. An empirical example is examined to demonstrate the…

处理簇很少时的野生自助法AI 解读

Inference based on cluster‐robust standard errors in linear regression models, using either the Student's t‐distribution or the wild cluster bootstrap, is known to fail when the number of treated clusters is very small. We propose a family of new procedures called the subcluster wild bootstrap, which includes the ordinary wild bootstrap as a limiting case. In the case of pure treatment models, where all observations within clusters are either treated or not, the latter…

利用 L 统计量对（条件）分位数差与分位距进行非参数推断AI 解读

We provide novel, high‐order accurate methods for non‐parametric inference on quantile differences between two populations in both unconditional and conditional settings. These quantile differences correspond to (conditional) quantile treatment effects under (conditional) independence of a binary treatment and potential outcomes. Our methods use the probability integral transform and a Dirichlet (rather than Gaussian) reference distribution to pick appropriate L‐statistics…

条件效率测度的中心极限定理与非参数两阶段生产模型的可分性检验AI 解读

In this paper, we demonstrate that standard central limit theorem (CLT) results do not hold for means of non‐parametric, conditional efficiency estimators, and we provide new CLTs that permit applied researchers to make valid inference about mean conditional efficiency or to compare mean efficiency across groups of producers. The new CLTs are used to develop a test of the restrictive ‘separability’ condition that is necessary for second‐stage regressions of efficiency…

非平稳波动下的自适应野生自助法单位根检验AI 解读

Recent research has emphasized that permanent changes in the innovation variance (caused by structural shifts or an integrated volatility process) lead to size distortions in conventional unit root tests. It has been shown how these size distortions can be resolved using the wild bootstrap. In this paper, we first derive the asymptotic power envelope for the unit root testing problem when the non‐stationary volatility process is known. Next, we show that under suitable…

利用大面板高频数据检验因子载荷矩阵的时不变性AI 解读

In this paper, we introduce a nonparametric test against the constancy of the factor loading matrix of a high-dimensional continuous-time factor model using high-frequency data. The central limit theorems on the test statistics with and without perturbation are established under the null hypothesis that the factor loading matrix is constant as time evolves. The tests perform well in size and power. Interestingly, the test statistic without perturbation converges at a rate…

跳跃存在性与跳跃强度路径依赖的检验AI 解读

In this paper, we develop a “jump test” for the null hypothesis that the probability of a jump is zero, building on earlier work by Aït-Sahalia (2002). The test is based on realized third moments, and uses observations over an increasing time span. The test offers an alternative to standard finite time span tests, and is designed to detect jumps in the data generating process rather than detecting realized jumps over a fixed time span. More specifically, we make two…

Cholesky-GARCH模型与时变条件贝塔的渐近理论AI 解读

This paper proposes a new model with time-varying slope coefficients. Our model, called CHAR, is a Cholesky-GARCH model, based on the Cholesky decomposition of the conditional variance matrix introduced by Pourahmadi (1999) in the context of longitudinal data. We derive stationarity and invertibility conditions and prove consistency and asymptotic normality of the Full and equation-by-equation QML estimators of this model. We then show that this class of models is useful to…

多值处理下受处理者效应的有效倾向得分回归估计AI 解读

When a multivalued treatment is randomly assigned conditional on observables, valid causal comparisons for a subpopulation treated at a particular treatment level require two propensity scores—one for the treated level and one for the counterfactual level. We contribute efficient propensity score regression estimators to a class of treatment effects for the treated, under the cases when the propensity scores are unknown and when they are known. Our efficient estimator…

含不良投入与产出的生产效率推断：潜在价格与最优方向AI 解读

Researchers employ the directional distance function (DDF) to estimate multiple-input and multiple-output production, firm inefficiency, and productivity growth. We relax restrictive assumptions by computing optimal directions subject to profit maximization and cost minimization, correct for the potential endogeneity of inputs and outputs, estimate latent prices for bad outputs, measure firms’ responses to shadow prices rather than actual prices, and introduce an unobserved…

大横截面下事后风险溢价估计与资产定价检验：回归校准法AI 解读

We propose a modification of the two-pass cross-sectional regression approach for estimating ex-post risk premia in linear asset pricing models, suitable for the case of large cross sections and short time series. Employing the regression-calibration method, we provide a beta correction method, which deals with the error-in-variables problem, based on which we construct an N-consistent estimator of ex-post risk premia and develop associated novel asset pricing tests.…

无限阶自回归过程动态面板估计的渐近推断AI 解读

In this paper we consider the estimation of a dynamic panel autoregressive (AR) process of possibly infinite order in the presence of individual effects. We employ double asymptotics under which both the cross-sectional sample size and the length of time series tend to infinity and utilize the sieve AR approximation with its lag order increasing with the sample size. We establish the consistency and asymptotic normality of the fixed effects estimator and propose a…

摘要暂缺

断点回归设计中的非参数推断AI 解读

This paper studies the validity of nonparametric tests used in the regression discontinuity design. The null hypothesis of interest is that the average treatment effect at the threshold in the so-called sharp design equals a pre-specified value. We first show that, under assumptions used in the majority of the literature, for any test the power against any alternative is bounded above by its size. This result implies that, under these assumptions, any test with nontrivial…

高维协变量下点过程预测的估计AI 解读

Estimation of the intensity of a point process is considered within a nonparametric framework. The intensity measure is unknown and depends on covariates, possibly many more than the observed number of jumps. Only a single trajectory of the counting process is observed. Interest lies in estimating the intensity conditional on the covariates. The impact of the covariates is modelled by an additive model where each component can be written as a linear combination of possibly…

多方程系统共同断点的检验AI 解读

The issue addressed in this paper is that of testing for common breaks across or within equations of a multivariate system. Our framework is very general and allows integrated regressors and trends as well as stationary regressors. The null hypothesis is that breaks in different parameters occur at common locations and are separated by some positive fraction of the sample size unless they occur across different equations. Under the alternative hypothesis, the break dates…

一致置信带的刻画与最优性AI 解读

This paper studies optimal uniform confidence bands for functions g(x,β0), where β0 is an unknown parameter vector. We provide a simple characterization of a general class of taut 1−α uniform confidence bands, allowing for both nonlinear functions and nonparametrically estimated functions. Specifically, we show that all taut bands can be obtained from projections on confidence sets for β0 and we characterize the class of sets which yield taut bands. Using these results, we…

存在零时间间隔的高频数据下积分波动率估计AI 解读

In estimating integrated volatility using high-frequency data, it is well documented that the presence of microstructure noise presents a major challenge. Recent literature has shown that the presence of multiple observations, a common feature in datasets, brings additional difficulty. In this study, we show that the preaveraging estimator is still consistent under multiple observations, and the related asymptotic distribution of the estimator is established. We also show…

多工具变量推断的最小距离方法AI 解读

I analyze a linear instrumental variables model with a single endogenous regressor and many instruments. I use invariance arguments to construct a new minimum distance objective function. With respect to a particular weight matrix, the minimum distance estimator is equivalent to the random effects estimator of Chamberlain and Imbens (2004), and the estimator of the coefficient on the endogenous regressor coincides with the limited information maximum likelihood estimator.…