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高维半参数高斯 Copula 向量自回归模型的估计与推断AI 解读

This paper develops simple, robust estimation and inference methods for the transition matrix of a high-dimensional semiparametric Gaussian copula vector autoregressive process. Our estimator is based on rank estimators of the large variance and auto-covariance matrices of a transformed latent high-dimensional Gaussian process. We derive rates of convergence of our estimator based on which we develop de-biased inference for Granger causality. Numerical results demonstrate…

具有双向不可观测异质性的线性面板回归AI 解读

We study linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects. In standard additive or interactive fixed effect models the individual specific and time specific effects are assumed to enter with a known functional form (additive or multiplicative). In this paper, we allow for this functional form to be more general and unknown. We discuss two different estimation approaches that allow consistent…

广义指数加权移动平均分位数模型及其统计推断AI 解读

The exponentially weighting scheme is a simple and pragmatic approach to compute the value at risk (VaR). However, the existing exponentially weighting methods lack a sound statistical inference procedure. To circumvent this deficiency, this paper proposes a new generalized exponentially weighted moving average (GEWMA) quantile model, which allows a much broader weighting scheme than the benchmark one used in “Risk Metrics” document. For the GEWMA quantile model, a…

协变量自适应随机化与不完全依从下的推断AI 解读

This paper studies inference in a randomized controlled trial (RCT) with covariate-adaptive randomization (CAR) and imperfect compliance of a binary treatment. In this context, we study inference on the local average treatment effect (LATE), i.e., the average treatment effect conditional on individuals that always comply with the assigned treatment. As in Bugni et al. (2018, 2019), CAR refers to randomization schemes that first stratify according to baseline covariates and…

识别间歇性的收益可预测性AI 解读

Return predictability has been one of the central research questions in finance for many decades. This paper proposes a predictive regression with multiple structural changes to capture the sporadic predictive ability of potential predictors for the return series. An adaptive group Lasso procedure, augmented with a forward regression for break screening, is adopted to efficiently and consistently identify the structural breaks in the predictive regression, with predictors…

博弈中的稳定结果与信息：一个实证框架AI 解读

Empirically, many strategic settings are characterized by stable outcomes in which players’ decisions are publicly observed, yet no player takes the opportunity to deviate. To analyze such situations in the presence of incomplete information, we build an empirical framework by introducing a novel solution concept that we call Bayes stable equilibrium and computationally tractable approaches for estimation and inference. Our framework allows the researcher to be agnostic…

动态离散选择混合模型的识别AI 解读

This paper provides new identification results for finite mixtures of Markov processes. Our arguments yield identification from knowledge of the cross-sectional distribution of three (or more) effective time-series observations under simple conditions. We explain how our approach and results are different from those in previous work by Kasahara and Shimotsu (2009) and Hu and Shum (2012). Most notably, outside information, such as monotonicity restrictions that link…

基于时序与信息集假设的结构模型识别不足AI 解读

We revisit identification based on timing and information set assumptions in structural models, which have been used in the context of production functions, demand equations, and hedonic pricing models (e.g. Olley and Pakes, 1996, Blundell and Bond, 2000). First, we demonstrate a general under-identification problem using these assumptions in a simple version of the Blundell–Bond dynamic panel model. In particular, the basic moment conditions can yield multiple discrete…

随机实验中溢出效应的识别与估计AI 解读

I study identification, estimation and inference for spillover effects in experiments where units’ outcomes may depend on the treatment assignments of other units within a group. I show that the commonly-used reduced-form linear-in-means regression identifies a weighted sum of spillover effects with some negative weights, and that the difference in means between treated and controls identifies a combination of direct and spillover effects entering with different signs. I…

通过模糊聚类回归近似分组固定效应估计AI 解读

Summary We propose a new, computationally efficient way to approximate the “grouped fixed effects” (GFE) estimator of Bonhomme and Manresa (2015), which estimates grouped patterns of unobserved heterogeneity. To do so, we generalize the fuzzy C‐means objective to regression settings. As the clustering exponent approaches 1, the fuzzy clustering objective converges to the GFE objective, which we recast as a standard generalized method of moments problem. We replicate the…

工具相关性时变下美国货币政策冲击的汇率效应与溢出识别AI 解读

Summary We propose a novel econometric approach to estimating time‐varying policy effects using external instruments in the presence of time‐varying instrument relevance in a factor‐augmented VAR model with data on the United States, Canada, Germany, Japan, and the United Kingdom. We find that US monetary policy shocks are an important driver of the exchange rate movements, with no delayed overshooting. We show that estimates of spillover effects of US monetary policy shocks…

向量自回归模型中 Kilian–Lewis 式反事实分析的直接方法AI 解读

Summary This paper proposes a direct approach to Kilian–Lewis style counterfactual analysis in structural vector autoregression models. The proposed approach is easy to implement, and the procedure of counterfactual construction is more transparent than the indirect approach. Practitioners conducting Kilian–Lewis style counterfactual analysis should find the proposed approach novel and useful.

对《两个数据集的故事：网络样本推断的代表性与可推广性》的讨论AI 解读

I congratulate the authors on their timely and insightful article. Since the advent of network analysis, there has been the question of the meaning of sample size in a network setting, which in the...

小型网络中的检验功效与多重共线性：关于网络样本推断的评论AI 解读

The recent work by Krivitsky, Coletti & Hens [KCH] provides an important new contribution to the Exponential-Family Random Graph Models [ERGMs], a start-to-finish approach to dealing with multi-network ERGMs. Although multi-network ERGMs have been around for a while (mostly in the form of block-diagonal models and multi-level ERGMs, see Duxbury and Wertsching (2023), Wang et al. (2013), Slaughter and Koehly (2016)), not much care has been given to the estimation and…

两个数据集的故事：网络样本推断的代表性与可推广性AI 解读

The last two decades have seen considerable progress in foundational aspects of statistical network analysis, but the path from theory to application is not straightforward.Two large, heterogeneous samples of small networks of within-household contacts in Belgium were collected using two different but complementary sampling designs: one smaller but with all contacts in each household observed, the other larger and more representative but recording contacts of only one person…

利用贝叶斯非参数共同原子回归构建临床试验合成对照AI 解读

The availability of electronic health records (EHR) has opened opportunities to supplement increasingly expensive and difficult to carry out randomized controlled trials (RCT) with evidence from readily available real world data. In this paper, we use EHR data to construct synthetic control arms for treatment-only single arm trials. We propose a novel nonparametric Bayesian common atoms mixture model that allows us to find equivalent population strata in the EHR and the…

关于《两个数据集的故事：网络样本推断的代表性与可推广性》的讨论AI 解读

The notions of sample and population have been at the heart of statistics since R.A. Fisher and others distinguished sample quantities from population quantities. While sample-to-population inferen...

分析多维数字表型的混合响应状态空间模型AI 解读

Digital technologies (e.g., mobile phones) can be used to obtain objective, frequent, and real-world digital phenotypes from individuals. However, modeling these data poses substantial challenges since observational data are subject to confounding and various sources of variabilities. For example, signals on patients' underlying health status and treatment effects are mixed with variation due to the living environment and measurement noises. The digital phenotype data thus…

容纳风险估计中的时变异质性：Cox 模型下的迁移学习AI 解读

Transfer learning has attracted increasing attention in recent years for adaptively borrowing information across different data cohorts in various settings. Cancer registries have been widely used in clinical research because of their easy accessibility and large sample size. Our method is motivated by the question of how to utilize cancer registry data as a complement to improve the estimation precision of individual risks of death for inflammatory breast cancer (IBC)…

大型电子健康记录分析：稀有事件下的最优 Cox 回归子抽样AI 解读

Massive sized survival datasets become increasingly prevalent with the development of the healthcare industry, and pose computational challenges unprecedented in traditional survival analysis use cases. In this work we analyze the UK-biobank colorectal cancer data with genetic and environmental risk factors, including a time-dependent coefficient, which transforms the dataset into “pseudo-observation” form, thus, critically inflating its size. A popular way for coping with…

利用提升式离散时间竞争风险模型分析老年患者术后死亡AI 解读

Elderly patients are at a high risk of suffering from postoperative death. Personalized strategies to improve their recovery after intervention are therefore urgently needed. A popular way to analyze postoperative mortality is to develop a prognostic model that incorporates risk factors measured at hospital admission, for example, comorbidities. When building such models, numerous issues must be addressed, including censoring and the presence of competing events (such as…

协变量引导的潜在交互模型：校正鸟类与植物互动预测的地理及分类偏差AI 解读

Reductions in natural habitats urge that we better understand species’ interconnection and how biological communities respond to environmental changes. However, ecological studies of species’ interactions are limited by their geographic and taxonomic focus which can distort our understanding of interaction dynamics. We focus on bird–plant interactions that refer to situations of potential fruit consumption and seed dispersal. We develop an approach for predicting species’…

什么是随机化检验？AI 解读

The meaning of randomization tests has become obscure in statistics education and practice over the last century.This article makes a fresh attempt at rectifying this core concept of statistics.A new term-"quasirandomization test"-is introduced to define significance tests based on theoretical models and distinguish these tests from the "randomization tests" based on the physical act of randomization.The practical importance of this distinction is illustrated through a real…

低秩高维向量自回归模型的多重变点检测AI 解读

We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structure. We first address the problem of detecting a single change point using an exhaustive search algorithm and establish a finite sample error bound for its accuracy. Next, we extend the results to the case of multiple change points that can grow as a function of the sample size. Their detection is…