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基于最优检验的结构断点日期置信集AI 解读

In this paper, we propose constructing a confidence set for the date of a one‐time structural change using a point optimal test. Following Elliott and Müller (2007, Journal of Econometrics 141, 1196–1218), we first construct a test for the break date that maximizes the weighted average of the power function. The confidence set is then obtained by inverting the test statistic. We carefully choose the weights and show by Monte Carlo simulations that the confidence set based on…

条件处理效应的非参数检验：单性别学校学业表现的应用AI 解读

We develop a general class of nonparametric tests for treatment effects conditional on covariates. We consider a wide spectrum of null hypotheses regarding conditional treatment effects, including the following: (a) the null hypothesis of the conditional stochastic dominance between treatment and control groups; (b) the null hypothesis that the conditional average treatment effect is nonpositive for each value of covariates; (c) the null hypothesis of no distributional (or…

N 固定时校正横截面依赖的新型面板协整检验AI 解读

In this paper, we propose new cointegration tests for single equations and panels. In both cases, the asymptotic distributions of the tests, which are derived with N fixed and T→∞, are shown to be standard normals. The effects of serial correlation and cross‐sectional dependence are mopped out via long‐run variances. An effective bias correction is derived, which is shown to work well in finite samples, particularly when N is smaller than T. Our panel tests are robust to…

匹配队列研究中的半参数因果推断AI 解读

Odds ratios can be estimated in case-control studies using standard logistic regression, ignoring the outcome-dependent sampling. In this paper we discuss an analogous result for treatment effects on the treated in matched cohort studies. Specifically, in studies where a sample of treated subjects is observed along with a separate sample of possibly matched controls, we show that efficient and doubly robust estimators of effects on the treated are computationally equivalent…

行列式点过程模型与统计推断AI 解读

Summary Statistical models and methods for determinantal point processes (DPPs) seem largely unexplored. We demonstrate that DPPs provide useful models for the description of spatial point pattern data sets where nearby points repel each other. Such data are usually modelled by Gibbs point processes, where the likelihood and moment expressions are intractable and simulations are time consuming. We exploit the appealing probabilistic properties of DPPs to develop parametric…

通过旋转使 Fisher 线性判别稀疏化AI 解读

Summary Many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis. To use them efficiently, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the sparsity needed. We propose a family of rotations to create the sparsity required. The basic idea is to use the principal components of the sample…

两水平析因设计的潜在结果因果推断AI 解读

Summary A framework for causal inference from two-level factorial designs is proposed, which uses potential outcomes to define causal effects. The paper explores the effect of non-additivity of unit level treatment effects on Neyman's repeated sampling approach for estimation of causal effects and on Fisher's randomization tests on sharp null hypotheses in these designs. The framework allows for statistical inference from a finite population, permits definition and…

存在截面依赖的半参数单指标面板模型AI 解读

In this paper, we consider a semiparametric single-index panel data model with cross-sectional dependence and stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and the link function for the case where both cross-sectional dimension (N) and temporal dimension (T) go…

空间自回归Tobit模型的最大似然估计AI 解读

This paper examines a Tobit model with spatial autoregressive interactions. We consider the maximum likelihood estimation for this model and analyze asymptotic properties of the estimator based on the spatial near-epoch dependence of the dependent variable process generated from the model structure. We show that the maximum likelihood estimator is consistent and asymptotically normally distributed. Monte Carlo experiments are performed to verify finite sample properties of…

自回归分布滞后框架中的分位数协整AI 解读

Xiao (2009) develops a novel estimation technique for quantile cointegrated time series by extending Phillips and Hansen’s (1990) semiparametric approach and Saikkonen’s (1991) parametrically augmented approach. This paper extends Pesaran and Shin’s (1998) autoregressive distributed-lag approach into quantile regression by jointly analyzing short-run dynamics and long-run cointegrating relationships across a range of quantiles. We derive the asymptotic theory and provide a…

不完全信息网络形成模型的两步估计AI 解读

We model network formation as a simultaneous game of incomplete information, allowing linking decisions to depend on the structure of the network as well as the attributes of agents. When the data is rationalized by a symmetric equilibrium, meaning observationally equivalent agents choose the same ex-ante strategies, the model can be estimated using a computationally simple two-step estimator. We derive its asymptotic properties under a sequence of models sending the number…

加性模型的设定与结构突变检验及其已实现方差应用AI 解读

We study two types of testing problems in a nonparametric additive model setting: We develop methods to test (i) whether an additive component function has a given parametric form and (ii) whether an additive component has a structural break. We apply the theory to a nonparametric extension of the linear heterogeneous autoregressive model which is widely employed to describe realized variance data. We find that the linearity assumption is often rejected, but actual…

极值估计与数值导数AI 解读

Finite-difference approximations are widely used in empirical work to evaluate derivatives of estimated functions. For instance, many standard optimization routines rely on finite-difference formulas for gradient calculations and estimating standard errors. However, the effect of such approximations on the statistical properties of the resulting estimators has only been studied in a few special cases. This paper investigates the impact of commonly used finite-difference…

模型误设下的结构突变模型及其预测含义AI 解读

This paper revisits the least squares estimator of the linear regression with a structural break. We view the model as an approximation to the true data generating process whose exact nature is unknown but perhaps changing over time either continuously or with some jumps. This view is widely held in the forecasting literature and under this view, the time series dependence property of all the observed variables is unstable as well. We establish that the rate of convergence…

分数单整过程筛法 Bootstrap 的高阶改进AI 解读

This paper investigates the accuracy of bootstrap-based inference in the case of long memory fractionally integrated processes. The re-sampling method is based on the semi-parametric sieve, whereby the dynamics in the process used to produce the bootstrap draws are captured by an autoregressive approximation. Application of the sieve method to data pre-filtered by a semi-parametric estimate of the long memory parameter is also explored. Higher-order improvements yielded by…

理解面板单位根检验局部渐近功效的新工具AI 解读

Motivated by the previously documented discrepancy between actual and predicted power, the present paper provides new tools for analyzing the local asymptotic power of panel unit root tests. These tools are appropriate in general when considering panel data with a dominant autoregressive root of the form ρi=1+ciN−κT−τ, where i=1,…,N indexes the cross-sectional units, T is the number of time periods and ci is a random local-to-unity parameter. A limit theory for the sample…

横截面异方差下动态面板变换似然估计的稳健标准误AI 解读

This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao et al. (2002) to the case where the errors are cross-sectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem and its implications for estimation and inference. We approach the problem by working with a mis-specified homoskedastic model, and then show that the transformed maximum likelihood estimator continues to be…

分位数回归的刀切法模型平均AI 解读

In this paper we consider model averaging for quantile regressions (QR) when all models under investigation are potentially misspecified and the number of parameters is diverging with the sample size. To allow for the dependence between the error terms and regressors in the QR models, we propose a jackknife model averaging (JMA) estimator which selects the weights by minimizing a leave-one-out cross-validation criterion function and demonstrate its asymptotic optimality in…

变换模型的非参数识别与估计AI 解读

This paper derives sufficient conditions for nonparametric transformation models to be identified and develops estimators of the identified components. Our nonparametric identification result is global, and allows for endogenous regressors. In particular, we show that a completeness assumption combined with conditional independence with respect to one of the regressors suffices for the model to be nonparametrically identified. The identification result is also constructive…

用相互激发跳跃过程建模金融传染AI 解读

We propose a model to capture the dynamics of asset returns, with periods of crises that are characterized by contagion. In the model, a jump in one region of the world increases the intensity of jumps both in the same region (self-excitation) as well as in other regions (cross-excitation), generating episodes of highly clustered jumps across world markets that mimic the observed features of the data. We develop and implement moment-based estimation and testing procedures…

基于 Fisher–Rao 度量的贝叶斯敏感性分析AI 解读

We propose a geometric framework to assess sensitivity of Bayesian procedures to modelling assumptions based on the nonparametric Fisher–Rao metric. While the framework is general, the focus of this article is on assessing local and global robustness in Bayesian procedures with respect to perturbations of the likelihood and prior, and on the identification of influential observations. The approach is based on a square-root representation of densities, which enables…

删失数据下非参数遗传风险函数的有效估计AI 解读

With an increasing number of causal genes discovered for complex human disorders, it is crucial to assess the genetic risk of disease onset for individuals who are carriers of these causal mutations and compare the distribution of age-at-onset with that in non-carriers. In many genetic epidemiological studies aiming at estimating causal gene effect on disease, the age-at-onset of disease is subject to censoring. In addition, some individuals' mutation carrier or non-carrier…

利用信息矩阵近似设计含随机区组效应的广义线性模型实验AI 解读

The selection of optimal designs for generalized linear mixed models is complicated by the fact that the Fisher information matrix, on which most optimality criteria depend, is computationally expensive to evaluate. We provide two novel approximations that reduce the computational cost of evaluating the information matrix by complete enumeration of response outcomes, or Monte Carlo approximations thereof: an asymptotic approximation that is accurate when there is strong…

单调性的非参数贝叶斯检验AI 解读

This paper adopts a nonparametric Bayesian approach to testing whether a function is monotone. Two new families of tests are constructed. The first uses constrained smoothing splines with a hierarchical stochastic-process prior that explicitly controls the prior probability of monotonicity. The second uses regression splines together with two proposals for the prior over the regression coefficients. Via simulation, the finite-sample performance of the tests is shown to…