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函数型时间序列的筛自助法AI 解读

A bootstrap procedure for functional time series is proposed which exploits a general vector autoregressive representation of the time series of Fourier coefficients appearing in the Karhunen–Loève expansion of the functional process. A double sieve-type bootstrap method is developed, which avoids the estimation of process operators and generates functional pseudo-time series that appropriately mimics the dependence structure of the functional time series at hand. The method…

Z 估计框架下多函数参数的正则化后一致有效置信区域AI 解读

In this paper, we develop procedures to construct simultaneous confidence bands for ${\tilde{p}}$ potentially infinite-dimensional parameters after model selection for general moment condition models where ${\tilde{p}}$ is potentially much larger than the sample size of available data, $n$. This allows us to cover settings with functional response data where each of the ${\tilde{p}}$ parameters is a function. The procedure is based on the construction of score functions that…

异方差误差下惩罚分位数回归的野生残差自助推断AI 解读

We consider a heteroscedastic regression model in which some of the regression coefficients are zero but it is not known which ones. Penalized quantile regression is a useful approach for analysing such data. By allowing different covariates to be relevant for modelling conditional quantile functions at different quantile levels, it provides a more complete picture of the conditional distribution of a response variable than mean regression. Existing work on penalized…

ArCo：高维面板时间序列的人工反事实方法AI 解读

We consider a new, flexible and easy-to-implement method to estimate thecausal effects of an intervention on a single treated unit when a control group is not available and which nests previous proposals in the literature. It is a two-step methodology where in the first stage, a counterfactual is estimated based on a large-dimensional set of variables from a pool of untreated units by means of shrinkage methods, such as the least absolute shrinkage and selection operator…

弱条件下的区块自助法一致性AI 解读

This paper weakens the size and moment conditions needed for typical block bootstrap methods (i.e., the moving blocks, circular blocks, and stationary bootstraps) to be valid for the sample mean of Near-Epoch-Dependent (NED) functions of mixing processes; they are consistent under the weakest conditions that ensure the original NED process obeys a central limit theorem (CLT), established by De Jong (1997, Econometric Theory 13(3), 353–367). In doing so, this paper extends De…

给定协变量的高维分位数关联网络的错误发现率控制AI 解读

Summary Motivated by gene coexpression pattern analysis, we propose a novel sample quantile contingency (SQUAC) statistic to infer quantile associations conditioning on covariates. It features enhanced flexibility in handling variables with both arbitrary distributions and complex association patterns conditioning on covariates. We first derive its asymptotic null distribution, and then develop a multiple-testing procedure based on the SQUAC statistic to test simultaneously…

条件异方差时间序列模型的混合分位数回归估计AI 解读

Summary Estimating conditional quantiles of financial time series is essential for risk management and many other financial applications. For time series models with conditional heteroscedasticity, although it is the generalized auto-regressive conditional heteroscedastic (GARCH) model that has the greatest popularity, quantile regression for this model usually gives rise to non-smooth non-convex optimization which may hinder its practical feasibility. The paper proposes an…

基于MCMC输出的模型设定检验AI 解读

Two test statistics are proposed to determine model specification after a model is estimated by an MCMC method. The first test is the MCMC version of IOSA test and its asymptotic null distribution is normal. The second test is motivated from the power enhancement technique of Fan et al. (2015). It combines a component (J1) that tests a null point hypothesis in an expanded model and a power enhancement component (J0) obtained from the first test. It is shown that J0 converges…

条件同方差下子集Anderson–Rubin检验的Bootstrap不一致与Bonferroni尺寸校正AI 解读

We focus on the linear instrumental variable model with two endogenous regressors under conditional homoskedasticity, and study the subset Anderson and Rubin (1949, AR) test when the nuisance structural parameter, the unrestricted slope coefficient of endogenous regressor, may be weakly identified. Weak identification leads to nonstandard null limiting distributions, and alternative to the usual chi-squared critical value is needed. We first investigate the bootstrap…

误差分布未知时反卷积的一致置信带AI 解读

This paper develops a method to construct uniform confidence bands in deconvolution when the error distribution is unknown. Simulation studies demonstrate the performance of the multiplier bootstrap confidence band in the finite sample. We apply our method to the Outer Continental Shelf (OCS) Auction Data and draw confidence bands for the density of common values of mineral rights on oil and gas tracts. We also present an application of our main theoretical result…

提高检验功效的随机单调性检验AI 解读

We develop improved statistical procedures for testing stochastic monotonicity. While existing tests use a fixed critical value to set the limiting rejection rate equal to nominal size at the least favorable case, we use a bootstrap procedure to raise the limiting rejection rate to nominal size over much of the null. This improves power against relevant local alternatives. To show the validity of our approach we draw on recent results on the directional differentiability of…

协变量自适应随机化下的推断AI 解读

This article studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum. Our main requirement is that the randomization scheme assigns treatment status within each stratum so that the fraction of units being…

利用价格分布的搜寻成本最小距离估计AI 解读

It has been shown that equilibrium restrictions in a search model can be used to identify quantiles of the search cost distribution from observedprices alone. These quantiles can be difficult to estimate in practice. This article uses a minimum distance approach to estimate them that is easy to compute. A version of our estimator is a solution to a nonlinear least-square problem that can be straightforwardly programmed on softwares such as STATA. We show our estimator is…

对称性条件下的条件均值独立检验AI 解读

Conditional mean independence (CMI) is one of the most widely used assumptions in the treatment effect literature to achieve model identification. We propose a Kolmogorov–Smirnov-type statistic to test CMI under a specific symmetry condition. We also propose a bootstrap procedure to obtain the p-values and critical values that are required to carry out the test. Results from a simulation study suggest that our test can work very well even in small to moderately sized…

反向嵌套描述子的渐近理论及干细胞分化推断AI 解读

For sequences of random backward nested subspaces as occur, say, in dimension reduction for manifold or stratified space valued data, asymptotic results are derived. In fact, we formulate our results more generally for backward nested families of descriptors (BNFD). Under rather general conditions, asymptotic strong consistency holds. Under additional, still rather general hypotheses, among them existence of a.s. local twice differentiable charts, asymptotic joint normality…

条件对称性的非参数检验AI 解读

We propose omnibus tests for symmetry of the conditional distribution of a time series process about a nonparametric regression function. The test statistic is a weighted version of the integrated squared difference between the restricted and unrestricted estimators of the joint characteristic function of nonparametric residuals and explanatory variables, whose critical values are estimated with the assistance of a bootstrap technique. The test is sensitive to local…

依赖结构限制下仅有两期数据的双重差分分位数处理效应AI 解读

This paper shows that the Conditional Quantile Treatment Effect on the Treated is identified under (i) a Conditional Distributional Difference in Differences assumption and (ii) a new assumption that the dependence (the copula) between the change in untreated potential outcomes and the initial level of untreated potential outcomes is the same for the treated group and untreated group. We consider estimation and inference with discrete covariates and propose a uniform…

一般函数不等式的检验AI 解读

In this article, we propose a general method for testing inequality restrictions on nonparametric functions. Our framework includes many nonparametric testing problems in a unified framework, with a number of possible applications in auction models, game theoretic models, wage inequality, and revealed preferences. Our test involves a one-sided version of L p functionals of kernel-type estimators (1 ≤ p < ∞ ) and is easy to implement in general, mainly due to its recourse to…

线性回归模型选择后推断的自助法方案AI 解读

We propose a general bootstrap recipe for estimating the distributions of post-model-selection least squares estimators under a linear regression model. The recipe constrains residual bootstrapping within the most parsimonious, approximately correct, models to yield a distribution estimator which is consistent provided any wrong candidate model is sufficiently separated from the approximately correct ones. Our theory applies to a broad class of model selection methods based…

局部平稳小波时间序列中无混叠或局部白噪声的检验AI 解读

Aliasing is often overlooked in time series analysis but can seriously distort the spectrum, the autocovariance and their estimates.We show that dyadic subsampling of a locally stationary wavelet process, which can cause aliasing, results in a process that is the sum of asymptotic white noise and another locally stationary wavelet process with a modified spectrum.We develop a test for the absence of aliasing in a locally stationary wavelet series at a fixed location, and…

方向可微函数的统计推断AI 解读

This article studies an asymptotic framework for conducting inference on parameters of the form $\phi(\theta_0)$, where $\phi$ is a known directionally differentiable function and $\theta_0$ is estimated by $\hat \theta_n$. In these settings, the asymptotic distribution of the plug-in estimator $\phi(\hat \theta_n)$ can be derived employing existing extensions to the Delta method. We show, however, that (full) differentiability of $\phi$ is a necessary and sufficient…

粒子滤波中的方差估计AI 解读

SummaryThis paper concerns numerical assessment of Monte Carlo error in particle filters. We show that by keeping track of certain key features of the genealogical structure arising from resampling operations, it is possible to estimate variances of a number of Monte Carlo approximations that particle filters deliver. All our estimators can be computed from a single run of a particle filter. We establish that, as the number of particles grows, our estimators are weakly…

估计的 Wold 表示与谱密度驱动的时间序列自助法AI 解读

Summary The second-order dependence structure of purely non-deterministic stationary processes is described by the coefficients of the famous Wold representation. These coefficients can be obtained by factorizing the spectral density of the process. This relationship together with some spectral density estimator is used to obtain consistent estimators of these coefficients. A spectral-density-driven bootstrap for time series is then developed which uses the entire sequence…

大型精度矩阵元素的置信区域AI 解读

We consider the statistical inference for high-dimensional precision matrices. Specifically, we propose a data-driven procedure for constructing a class of simultaneous confidence regions for a subset of the entries of a large precision matrix. The confidence regions can be applied to test for specific structures of a precision matrix, and to recover its nonzero components. We first construct an estimator for the precision matrix via penalized node-wise regression. We then…