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Comparing econometric methods to empirically evaluate activation programs for job seekers

求职者就业促进项目的计量评估方法比较

Summary We test whether different identification strategies give similar results when evaluating activation programs. Budgetary problems at the Dutch unemployment insurance (UI) administration in March 2010 caused a sharp drop in the availability of these programs. Using administrative data provided by the UI administration, we evaluate the effect of the program (1) exploiting the policy discontinuity as a quasi‐experiment, (2) using dynamic matching assuming conditional…

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Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction

建模研究间异质性以提高基因特征选择与临床预测的可重复性

In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach…

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Estimating Optimal Dynamic Treatment Regimes With Survival Outcomes

生存结局下最优动态治疗策略的估计

Gabrielle Simoneaua* , Erica E. M. Moodiea , Jagtar S. Nijjarb, Robert W. Platta & the Scottish Early Rheumatoid Arthritis Inception Cohort Investigatorsa Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; b Department of Medicine, University of Cambridge, Cambridge, UKColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article…

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Bayesian Model Search for Nonstationary Periodic Time Series

非平稳周期时间序列的贝叶斯模型搜索

We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behavior. We approximate the time series using a piecewise oscillatory model with unknown periodicities, where our goal is to estimate the change-points while simultaneously identifying the potentially changing periodicities in the data. Our proposed methodology is based on a trans-dimensional Markov chain Monte Carlo algorithm that simultaneously updates the…

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Feature Selection by Canonical Correlation Search in High-Dimensional Multiresponse Models With Complex Group Structures

复杂分组高维多响应模型的典型相关搜索特征选择

High-dimensional multiresponse models with complex group structures in both the response variables and the covariates arise from current researches in important fields such as genetics and medicine. However, no enough research has been done on such models. One of a few researches, if not the only one, is the article by Li, Nan, and Zhu where the sparse group Lasso approach is extended to such models. In this article, we propose a novel approach named the sequential canonical…

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External Validity in Fuzzy Regression Discontinuity Designs

模糊回归不连续设计的外部有效性

Fuzzy regression discontinuity designs identify the local average treatment effect (LATE) for the subpopulation of compliers, and with forcing variable equal to the threshold. We develop methods that assess the external validity of LATE to other compliance groups at the threshold, and allow for identification away from the threshold. Specifically, we focus on the equality of outcome distributions between treated compliers and always-takers, and between untreated compliers…

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Stationary Points for Parametric Stochastic Frontier Models

参数随机前沿模型的驻点

Stationary point results on the normal–half-normal stochastic frontier model are generalized using the theory of the Dirac delta, and distribution-free conditions are established to ensure a stationary point in the likelihood as the variance of the inefficiency distribution goes to zero. Stability of the stationary point and “wrong skew” results are derived or simulated for common parametric assumptions on the model. We discuss identification and extensions to more general…

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Identification and Estimation in Non-Fundamental Structural VARMA Models

非基本结构VARMA模型的识别与估计

Abstract The basic assumption of a structural vector autoregressive moving average (SVARMA) model is that it is driven by a white noise whose components are uncorrelated or independent and can be interpreted as economic shocks, called “structural” shocks. When the errors are Gaussian, independence is equivalent to non-correlation and these models face two identification issues. The first identification problem is “static” and is due to the fact that there is an infinite…

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On Bandwidth Choice for Spatial Data Density Estimation

空间数据密度估计的带宽选择

Summary Bandwidth choice is crucial in spatial kernel estimation in exploring non-Gaussian complex spatial data. The paper investigates the choice of adaptive and non-adaptive bandwidths for density estimation given data on a spatial lattice. An adaptive bandwidth depends on local data and hence adaptively conforms with local features of the spatial data. We propose a spatial cross-validation (SCV) choice of a global bandwidth. This is done first with a pilot density…

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Causal Mediation Analysis for Stochastic Interventions

随机干预的因果中介分析

Summary Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects. We present an analogous decomposition of the population intervention effect, defined through stochastic interventions on the exposure. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be…

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Nonparametric analysis of a duration model with stochastic unobserved heterogeneity

具有随机未观测异质性的持续期模型非参数分析

This paper develops nonparametric identification and estimation results for a single-spell hazard model, where the unobserved heterogeneity is specified as a Lévy subordinator. The identification approach solves a nonlinear Volterra integral equation of the first kind with an unknown kernel function. Both the kernel of the integral operator, which models the distribution of the unobserved heterogeneity, and the functions that enter it are identified given regularity…

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Inference for high-dimensional instrumental variables regression

高维工具变量回归的统计推断

This paper concerns statistical inference for the components of a high-dimensional regression parameter despite possible endogeneity of each regressor. Given a first-stage linear model for the endogenous regressors and a second-stage linear model for the dependent variable, we develop a novel adaptation of the parametric one-step update to a generic second-stage estimator. We provide conditions under which the scaled update is asymptotically normal. We then introduce a…

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High frequency traders and the price process

高频交易者与价格过程

Using a dataset that uniquely identifies counterparties to each S&P500 eMini transaction, we classify each market participant as high or low frequency, and each transaction, by the speed of the traders involved. We investigate empirically the comparative influence of high and low frequency traders on the price process, and conversely the influence of the price process on the trading of high and low frequency traders. We find that high frequency traders have a particularly…

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Estimating derivatives of function-valued parameters in a class of moment condition models

一类矩条件模型中函数值参数的导数估计

We develop a general approach to estimating the derivative of a function-valued parameter θo(u) that is identified for every value of u as the solution to a moment condition. This setup in particular covers interesting models for conditional distributions, such as quantile regression or distribution regression. Exploiting that θo(u) solves a moment condition, we obtain an explicit expression for its derivative from the Implicit Function Theorem, and then estimate the…

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Testing in high-dimensional spiked models

高维尖峰模型中的假设检验

We consider the five classes of multivariate statistical problems identified by James (Ann. Math. Stat. 35 (1964) 475–501), which together cover much of classical multivariate analysis, plus a simpler limiting case, symmetric matrix denoising. Each of James’ problems involves the eigenvalues of $E^{-1}H$ where $H$ and $E$ are proportional to high-dimensional Wishart matrices. Under the null hypothesis, both Wisharts are central with identity covariance. Under the…

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Counterfactual prediction in complete information games: Point prediction under partial identification

完全信息博弈的反事实预测:部分识别下的点预测

We study the problem of counterfactual prediction in discrete decision games with complete information, pure strategies, and Nash equilibria: the presence of multiple equilibria poses unique challenges. We introduce multiple types of counterfactuals to establish sharp identified bounds for their prediction probabilities. We propose and compare various point prediction methods, namely midpoint prediction, an approach using a Dirichlet-based prior, a maximum entropy method,…

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Unobserved heterogeneity in auctions under restricted stochastic dominance

受限随机占优下拍卖中的不可观测异质性

We study the identification of first-price auctions with nonseparable unobserved heterogeneity. In particular, we extend Hu et al. (2013) by relaxing the first-order stochastic dominance condition. Instead, we assume restricted stochastic dominance relations among value quantile functions and show that the same relations pass to bid quantile functions. An ordered tree summarizes these relations and provides a total ordering. Relying on the proposed restricted stochastic…

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Efficient GMM Estimation with Incomplete Data

不完整数据下的有效GMM估计

In the standard missing data model, data are either complete or completely missing. However, applied researchers face situations with an arbitrary number of strata of incompleteness. Examples include unbalanced panels and instrumental variables settings where some observations are missing some instruments. I propose a model for settings where observations may be incomplete, with an arbitrary number of strata of incompleteness. I derive a set of moment conditions that…

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IDENTIFICATION AND ESTIMATION IN A THIRD-PRICE AUCTION MODEL

第三价格拍卖模型的识别与估计

The first novelty of this paper is that we show global identification of the private values distribution in a sealed-bid third-price auction model using a fully nonparametric methodology. The second novelty of the paper comes from the study of the identification and estimation of the model using a quantile approach. We consider an i.i.d. private values environment with risk-averse bidders. In the first place, we consider the case where the risk-aversion parameter is known.…

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Estimation of firm‐level productivity in the presence of exports: Evidence from China's manufacturing

存在出口时的企业生产率估计:中国制造业证据

Summary Motivated by the long‐standing interest of economists in understanding the nexus between firm productivity and export behavior, this paper develops a novel structural framework for control‐function‐based nonparametric identification of the gross production function and latent firm productivity in the presence of endogenous export opportunities that is robust to recent unidentification critiques of proxy estimators. We provide a workable identification strategy,…

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Partial identification in nonseparable count data instrumental variable models

非可分计数数据工具变量模型的部分识别

Summary This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical examples show that the size of an identified set can be very small…

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Threshold factor models for high-dimensional time series

高维时间序列的门限因子模型

We consider a threshold factor model for high-dimensional time series in which the dynamics of the time series is assumed to switch between different regimes according to the value of a threshold variable. This is an extension of threshold modeling to a high-dimensional time series setting under a factor structure. Specifically, within each threshold regime, the time series is assumed to follow a factor model. The regime switching mechanism creates structural changes in the…

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Nonparametric Instrumental Variable Methods for Dynamic Treatment Evaluation

动态处理效应评估的非参数工具变量方法

We develop an instrumental variable approach for identification of dynamic treatment effects on survival outcomes in the presence of dynamic selection, noncompliance, and right-censoring. The approach is nonparametric and does not require independence of observed and unobserved characteristics or separability assumptions. We propose estimation procedures and derive asymptotic properties. We apply our approach to evaluate a policy reform in which the pathway of unemployment…

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MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments

MIMIX:用于设计实验微生物组数据的贝叶斯混合效应模型

Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of…

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