论文检索

测量不准的混杂因素放在回归左侧更有用AI 解读

Researchers frequently test identifying assumptions in regression-based research designs (which include instrumental variables or difference-in-differences models) by adding additional control variables on the right-hand side of the regression. If such additions do not affect the coefficient of interest (much), a study is presumed to be reliable. We caution that such invariance may result from the fact that the observed variables used in such robustness checks are often poor…

跳跃事件中的秩检验AI 解读

We propose a test for the rank of a cross-section of processes at a set of jump events. The jump events are either specific known times or are random and associated with jumps of some process. The test is formed from discretely sampled data on a fixed time interval with asymptotically shrinking mesh. In the first step, we form nonparametric estimates of the jump events via thresholding techniques. We then compute the eigenvalues of the outer product of the cross-section of…

利用工具变量检验随机缺失AI 解读

This article proposes a test for missing at random (MAR). The MAR assumption is shown to be testable given instrumental variables which are independent of response given potential outcomes. A nonparametric testing procedure based on integrated squared distance is proposed. The statistic’s asymptotic distribution under the MAR hypothesis is derived. In particular, our results can be applied to testing missing completely at random (MCAR). A Monte Carlo study examines finite…

偏倚抽样下二元生存数据的半参数模型AI 解读

Summary To understand better the relationship between patient characteristics and their residual survival after an intermediate event such as the local recurrence of cancer, it is of interest to identify patients with the intermediate event and then to analyse their residual survival data. One challenge in analysing such data is that the observed residual survival times tend to be longer than those in the target population, since patients who die before experiencing the…

定性因子实验中高斯随机场的信号混杂AI 解读

Signal aliasing is an inevitable consequence of using fractional factorial designs. Unlike linear models with fixed factorial effects, for Gaussian random field models advocated in some Bayesian design and computer experiment literature, the issue of signal aliasing has not received comparable attention. In the present article, this issue is tackled for experiments with qualitative factors. The signals in a Gaussian random field can be characterized by the random effects…

广义随机森林AI 解读

We propose generalized random forests, a method for nonparametric statistical estimation based on random forests (Breiman [Mach. Learn. 45 (2001) 5–32]) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method considers a weighted set of nearby training examples; however, instead of using classical kernel weighting functions that are prone to a…

误分类二元内生解释变量效应的识别AI 解读

This paper studies identification of the effect of a mis-classified, binary, endogenous regressor when a discrete-valued instrumental variable is available. We begin by showing that the only existing point identification result for this model is incorrect. We go on to derive the sharp identified set under mean independence assumptions for the instrument and measurement error. The resulting bounds are novel and informative, but fail to point identify the effect of interest.…

随机权重矩阵下空间自回归模型的估计AI 解读

We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weight matrices. Allowing a general spatial linear process form for the disturbances that permits many common types of error specifications as well as potential ‘long memory’, we provide sufficient conditions for consistency and asymptotic normality of instrumental variables, ordinary least squares, and pseudo maximum likelihood estimates. The implications of popular weight matrix…

不同波动率状态下的不确定性AI 解读

Summary We propose a nonrecursive identification scheme for uncertainty shocks that exploits breaks in the volatility of macroeconomic variables and is novel in the literature on uncertainty. This approach allows us to simultaneously address two major questions in the empirical literature: Is uncertainty a cause or effect of decline in economic activity? Does the relationship between uncertainty and economic activity change across macroeconomic regimes? Results based on a…

参与者信念不处于均衡时动态博弈的识别与估计AI 解读

This article deals with the identification and estimation of dynamic games when players’ beliefs about other players’ actions are biased, that is, beliefs do not represent the probability distribution of the actual behaviour of other players conditional on the information available. First, we show that an exclusion restriction, typically used to identify empirical games, provides testable non-parametric restrictions of the null hypothesis of equilibrium beliefs in dynamic…

从函数数据片段恢复协方差AI 解读

We consider nonparametric estimation of a covariance function on the unit square, given a sample of discretely observed fragments of functional data. When each sample path is observed only on a subinterval of length |$\delta<1$|⁠, one has no statistical information on the unknown covariance outside a |$\delta$|-band around the diagonal. The problem seems unidentifiable without parametric assumptions, but we show that nonparametric estimation is feasible under suitable…

通过约束似然重建高斯有向无环图AI 解读

Directed acyclic graphs are widely used to describe directional pairwise relations. Such relations are estimated by reconstructing a directed acyclic graph's structure, which is challenging when the ordering of nodes of the graph is unknown. In such a situation, existing methods such as the neighbourhood and search-and-score methods have high estimation errors or computational complexities, especially when a local or sequential approach is used to enumerate edge directions…

利用隐马尔可夫模型 knockoff 变量寻找关联基因AI 解读

Modern scientific studies often require the identification of a subset of explanatory variables. Several statistical methods have been developed to automate this task, and the framework of knockoffs has been proposed as a general solution for variable selection under rigorous Type I error control, without relying on strong modelling assumptions. In this paper, we extend the methodology of knockoffs to problems where the distribution of the covariates can be described by a…

随机议价模型识别的新结果AI 解读

We present new identification results for stochastic sequential bargaining models when the data only reports the time of agreement and the evolution of observable states. With no information on the stochastic surplus available for allocation or how it is allocated under agreement, we recover the latent surplus process, the distribution of unobservable states, and the equilibrium outcome in counterfactual contexts. The method we propose, which is constructive and original,…

双变量Probit模型、极大似然、伪真参数与部分识别AI 解读

This paper examines the notion of “identification by functional form” for two equation triangular systems for binary endogenous variables by providing a bridge between the literature on the recursive bivariate probit model and that on partial identification. We evaluate the impact of functional form on the performance of (quasi) maximum likelihood estimators, and investigate the practical importance of available instruments in both cases of correct and incorrect…

利用局部工具变量曲线进行连续工具变量的稳健因果推断AI 解读

Summary Instrumental variables are commonly used to estimate effects of a treatment afflicted by unmeasured confounding, and in practice instruments are often continuous (e.g. measures of distance, or treatment preference). However, available methods for continuous instruments have important limitations: they either require restrictive parametric assumptions for identification, or else rely on modelling both the outcome and the treatment process well (and require modelling…

局部自适应置信带AI 解读

We develop honest and locally adaptive confidence bands for probability densities. They provide substantially improved confidence statements in case of inhomogeneous smoothness, and are easily implemented and visualized. The article contributes conceptual work on locally adaptive inference as a straightforward modification of the global setting imposes severe obstacles for statistical purposes. Among others, we introduce a statistical notion of local Hölder regularity and…

通过矩阵补全进行函数型数据分析AI 解读

Functional data analyses typically proceed by smoothing, followed by functional PCA. This paradigm implicitly assumes that rough variation is due to nuisance noise. Nevertheless, relevant functional features such as time-localised or short scale fluctuations may indeed be rough relative to the global scale, but still smooth at shorter scales. These may be confounded with the global smooth components of variation by the smoothing and PCA, potentially distorting the parsimony…

含未观测随机动态效应的非线性时间序列与面板模型的拉普拉斯变换识别AI 解读

We consider nonlinear parametric and semi-parametric models for time series and panel data including unobserved dynamic effects. These regression models have an affine specification with respect to lagged endogenous variables and unobserved dynamic effects. We derive conditional moment restrictions based on suitable Laplace transforms. We show how to deploy these nonlinear moment restrictions to identify the parameters of the affine regression model, and the parametric or…

工具变量不足时多内生变量三角模型的识别与估计AI 解读

We develop a novel identification method for a partially linear model with multiple endogenous variables of interest but a single instrumental variable, which could even be binary. We present an easy-to-implement consistent estimator for the parametric part. This estimator retains n-convergence rate and asymptotic normality even though we have a generated regressor in our setup. The nonparametric part of the model is also identified. We also outline how our identification…

内生误报下的处理效应估计AI 解读

Participation in social programs is often misreported in survey data, complicating the estimation of treatment effects. We propose a model to estimate treatment effects under endogenous participation and endogenous misreporting. We present an expression for the asymptotic bias of both OLS and IV estimators and discuss the conditions under which sign reversal may occur. We provide a method for eliminating this bias when researchers have access to information regarding…

民主确实促进增长AI 解读

We provide evidence that democracy has a positive effect on GDP per capita. Our dynamic panel strategy controls for country fixed effects and the rich dynamics of GDP, which otherwise confound the effect of democracy. To reduce measurement error, we introduce a new indicator of democracy that consolidates previous measures. Our baseline results show that democratizations increase GDP per capita by about 20 percent in the long run. We find similar effects using a propensity…

存在内生性时半参数变换模型的估计AI 解读

We consider a semiparametric transformation model, in which the regression function has an additive nonparametric structure and the transformation of the response is assumed to belong to some parametric family. We suppose that endogeneity is present in the explanatory variables. Using a control function approach, we show that the proposed model is identified under suitable assumptions, and propose a profile estimation method for the transformation. The proposed estimator is…

移动应用激活数据的捕获—再捕获方法AI 解读

This work is concerned with the analysis of marketing data on the activation of applications (apps) on mobile devices. Each application has a hashed identification number that is specific to the device on which it has been installed. This number can be registered by a platform at each activation of the application. Activations on the same device are linked together using the identification number. By focusing on activations that took place at a business location, one can…