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非线性面板中潜在分组结构的识别AI 解读

We propose a procedure to identify latent group structures in nonlinear panel data models where some regression coefficients are heterogeneous across groups but homogeneous within a group and the group number and membership are unknown. To identify the group structures, we consider the order statistics for the preliminary unconstrained consistent estimators of the regression coefficients and translate the problem of classification into the problem of break detection. Then we…

非线性固定效应面板模型的二阶似然修正AI 解读

We propose a second-order correction for nonlinear fixed-effect panel models. The correction is made via the log-likelihood function. It removes the two leading terms of the bias of the log-likelihood that arises from estimating the fixed effects. Maximizing the corrected likelihood gives a second-order bias-corrected estimator, with bias OT−3, where T is the number of time periods. The corrected likelihood also gives second-order corrected test statistics. The correction…

面板离散响应模型的半参数识别AI 解读

This paper studies semiparametric identification in linear index discrete response panel data models with fixed effects. Departing from the classic binary response static panel data model, this paper examines identification in the binary response dynamic panel data model and the ordered response static panel data model. It is shown that under mild distributional assumptions on the fixed effect and the time-varying unobservables point-identification fails, but informative…

面板数据模型中的异质结构突变AI 解读

This paper develops a new model and estimation procedure for panel data that allows us to identify heterogeneous structural breaks. We model individual heterogeneity using a grouped pattern. For each group, we allow common structural breaks in the coefficients. However, the number, timing, and size of these breaks can differ across groups. We develop a hybrid estimation procedure of the grouped fixed effects approach and adaptive group fused Lasso. We show that our method…

大规模面板中异质性与协整的推断理论AI 解读

This paper provides an estimation and testing framework to assess the presence and the extent of slope heterogeneity and cointegration when the units are a mixture of spurious and/or cointegrating regressions. We propose two moment estimators for the degree of heterogeneity (measured by the dispersion of the slope coefficients around their average) and for the fraction of spurious regressions, which are found to be consistent in the whole parameter space. Based on these…

多层模型估计的计量经济学方法AI 解读

In this paper we consider “multidimensional” or “hierarchical” or “multilevel” models that are popular in the educational and economics literatures. Instead of two levels (individuals over time in the standard panel data model), we now have multiple levels (e.g. students in classrooms in schools in districts). We apply standard methods of analysis for econometric panel data to multilevel models. Specifically, we generalize the results of Hausman and Taylor and the subsequent…

预测 VIX 与波动率风险溢价：短期融资利差波动因子的作用AI 解读

This paper presents an innovative approach to extract Volatility Factors which predict the VIX, the S&P500 Realized Volatility (RV) and the Variance Risk Premium (VRP). The approach is innovative along two different dimensions, namely: (1) we extract Volatility Factors from panels of filtered volatilities — in particular large panels of univariate ARCH-type models and propose methods to estimate common Volatility Factors in the presence of estimation error and (2) we price…

具有系统性斜率变化的异质面板估计AI 解读

We analyse estimation procedures for the panel data models with heterogeneous slopes. Specifically we take into account a possible dependence between regressors and heterogeneous slope coefficients, which is referred to as systematic variation. It is shown that under relevant forms of systematic slope variations (i) the pooled OLS estimator is severely biased, (ii) Swamy’s GLS estimator is inconsistent if the number of time periods T is fixed, whereas (iii) the mean-group…

变维模型的最优辅助先验与可逆跳跃提议AI 解读

This article develops a Markov chain Monte Carlo (MCMC) method for a class of models that encompasses finite and countable mixtures of densities and mixtures of experts with a variable number of mixture components. The method is shown to maximize the expected probability of acceptance for cross-dimensional moves and to minimize the asymptotic variance of sample average estimators under certain restrictions. The method can be represented as a retrospective sampling algorithm…

潜变量非参数协整回归AI 解读

This article studies the asymptotic properties of empirical nonparametric regressions that partially misspecify the relationships between nonstationary variables. In particular, we analyze nonparametric kernel regressions in which a potential nonlinear cointegrating regression is misspecified through the use of a proxy regressor in place of the true regressor. Such models occur in linear and nonlinear regressions where the regressor suffers from measurement error or where…

时间序列模型假设检验的自助法导论：双自回归模型应用AI 解读

In this article, we discuss the bootstrap as a tool for statistical inference in econometric time series models. Importantly, in the context of testing, properties of the bootstrap under the null (size) as well as under the alternative (power) are discussed. Although properties under the alternative are crucial to ensure the consistency of bootstrap-based tests, it is often the case in the literature that only validity under the null is discussed. We provide new results on…

处理状态误分类下的工具变量分位数回归AI 解读

Abstract This article investigates the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, Econometrica 73, 245–261; 2013, Annual Review of Economics , 5, 57–81) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first result is that, remarkably, the reduced-form quantile regression of the outcome variable on the instrumental variable provides a lower bound on the…

利用高维协变量特异性治疗效应曲线估计最优个体化治疗规则AI 解读

With a large number of baseline covariates, we propose a new semiparametric modeling strategy for heterogeneous treatment effect estimation and individualized treatment selection, which are two major goals in personalized medicine. We achieve the first goal through estimating a covariate-specific treatment effect (CSTE) curve modeled as an unknown function of a weighted linear combination of all baseline covariates. The weight or the coefficient for each covariate is…

无直接效应假设下最优策略的有效估计AI 解读

Lin Liua , Zach Shahnb, James M. Robinsc* & Andrea Rotnitzkyda Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC and SJTU-Yale Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China; b IBM Research, Yorktown Heights, NY; c Department of Biostatistics and Epidemiology, Harvard University, Boston, MA; d Department of Economics, Universidad Torcuato Di Tella and CONICET, Buenos Aires, ArgentinaSupplementary materials for…

无限维、零直接样本情形的多分辨率近似理论：过渡推断与个体化预测AI 解读

Transitional inference is an empiricism concept, rooted and practiced in clinical medicine since ancient Greece. Knowledge and experiences gained from treating one entity (e.g., a disease or a group of patients) are applied to treat a related but distinctively different one (e.g., a similar disease or a new patient). This notion of “transition to the similar” renders individualized treatments an operational meaning, yet its theoretical foundation defies the familiar…

学习何时开始治疗的动态政策AI 解读

Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many visits from a patient. We develop an “advantage doubly robust” estimator for learning such dynamic treatment rules using observational data under the assumption…

用于估计最优动态治疗策略的随机树搜索AI 解读

Yilun Sunab & Lu Wanga*a Department of Biostatistics, University of Michigan, Ann Arbor, MI; b Department of Radiation Oncology, University of Michigan, Ann Arbor, MISupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Lu Wang luwang@umich.edu Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109.AbstractA dynamic treatment regime (DTR) is a sequence of decision rules that adapt to the time-varying…

针对多领域潜在结局的个体化治疗规则学习AI 解读

For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they may also serve as more reliable and representative features to differentiate treatment responses. Therefore, in order to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning…

融合 GWAS 汇总统计与外部信息的多基因风险惩罚回归AI 解读

Large-scale genome-wide association studies (GWAS) provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance for a given disease, which typically requires a large training dataset for selecting truly associated single nucleotide polymorphisms (SNPs) and estimating effect sizes accurately.…

长期平均结果的离策略估计及其移动健康应用AI 解读

Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to impact a near time, proximal outcome such as stress or physical activity. The mHealth intervention policies, often called just-in-time adaptive interventions,…

从半连续结局估计个体化处理规则的两部分框架AI 解读

Jared D. Hulinga , Maureen A. Smithb & Guanhua Chenc* a Division of Biostatistics, University of Minnesota, Minneapolis, MN; b Departments of Population Health Sciences and Family Medicine, University of Wisconsin-Madison, Madison, WI; c Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WISupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Jared Huling huling@umn.edu…

学习最优个体化治疗规则的改进双重稳健估计AI 解读

Individualized treatment rules (ITRs) recommend treatment according to patient characteristics. There is a growing interest in developing novel and efficient statistical methods in constructing ITRs. We propose an improved doubly robust estimator of the optimal ITRs. The proposed estimator is based on a direct optimization of an augmented inverse-probability weighted estimator (AIPWE) of the expected clinical outcome over a class of ITRs. The method enjoys two key…

利用纵向移动健康数据学习个性化干预策略AI 解读

Personalized policy represents a paradigm shift from one-decision-rule-for-all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications imposes challenges. First, for lack of adherence, data from each user are limited. Second, unmeasured contextual factors can potentially impact on decision making. Aiming to optimize immediate rewards, we propose using a generalized linear mixed modeling framework where population…

内生性下最优治疗规则的半参数工具变量方法AI 解读

There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable (IV) approach to learning optimal treatment regimes under endogeneity. Specifically,…