每日简报

高维时间序列的频域统计推断AI 解读

Analyzing time series in the frequency domain enables the development of powerful tools for investigating the second-order characteristics of multivariate processes. Parameters like the spectral density matrix and its inverse, the coherence or the partial coherence, encode comprehensively the complex linear relations between the component processes of the multivariate system. In this paper, we develop inference procedures for such parameters in a high-dimensional, time…

利用机器学习估计观测网络数据中的处理效应AI 解读

Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between them. We develop augmented inverse probability weighting (AIPW) for estimation and inference of the expected average treatment effect (EATE) with observational data from a single (social) network with spillover effects. In contrast to overall effects such as the global…

基于机器学习的去偏 U 统计量AI 解读

We propose a method to debias estimators based on U-statistics with machine-learning (ML) first steps. Standard plug-in estimators often suffer from regularization and model-selection biases, leading to invalid inference. We characterize orthogonal adjustment terms for two-step U-statistics, construct Debiased Machine Learning (DML) U-estimators, develop a cross-fitting algorithm, and establish a general asymptotic theory for inference with ML first steps. We illustrate the…

替代后代机制下带移民的修正 Galton–Watson 过程AI 解读

We propose a novel class of count time series models alternative to the classic Galton-Watson process with immigration (GWI) and Bernoulli offspring. A new offspring mechanism is developed and its properties are explored. This novel mechanism, called geometric thinning operator, is used to define a class of modified GWI (MGWI) processes, which induces a certain non-linearity to the models. We show that this non-linearity can produce better results in terms of prediction when…

Shift-Share 设计的随机化推断检验AI 解读

We consider the problem of inference in shift-share research designs. The choice between existing approaches that allow for unrestricted spatial correlation involves tradeoffs, varying in terms of their validity when there are relatively few or concentrated shocks, and in terms of the assumptions on the shock assignment process and treatment effects heterogeneity. We propose alternative randomization inference methods that combine the advantages of different approaches.…

迪拜原油与美国天然气市场的协整关系及 ARDL 设定AI 解读

This paper examines the relationship between the price of the Dubai crude oil and the price of the US natural gas using an updated monthly dataset from 1992 to 2018, incorporating the latter events in the energy markets. After employing a variety of unit root and cointegration tests, the long-run relationship is examined via the autoregressive distributed lag (ARDL) cointegration technique, along with the Toda-Yamamoto (1995) causality test. Our results indicate that there…

抽样变异对估计所得分布预测组合的影响AI 解读

We investigate the performance and sampling variability of estimated forecast combinations, with particular attention given to the combination of forecast distributions. Unknown parameters in the forecast combination are optimized according to criterion functions based on proper scoring rules, which are chosen to reward the form of forecast accuracy that matters for the problem at hand, and forecast performance is measured using the out-of-sample expectation of said scoring…

处理变量存在测量误差时的因果推断：非参数工具变量方法AI 解读

We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error. We do so by generalizing estimation in the instrumental variable setting. Despite significant work on regression with measurement error, additionally handling unobserved confounding in the continuous setting is non-trivial: we have seen little prior work. As a by-product of our investigation, we clarify a connection between mean embeddings and characteristic…

含外生输入的广义小波矩方法：GNSS 位置时间序列的快速分析AI 解读

The Global Navigation Satellite System (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow studying global and local geodynamical effects such as plate tectonics, earthquakes, or ground water variations. In this work we propose to extend the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of linear models with correlated residuals. This statistical inferential framework is…

差异性选择下聚类观察研究因果效应的非参数识别AI 解读

The clustered observational study (COS) design is the observational study counterpart to the clustered randomized trial. In a COS, a treatment is assigned to intact groups, and all units within the group are exposed to the treatment. However, the treatment is non-randomly assigned. COSs are common in both education and health services research. In education, treatments may be given to all students within some schools but withheld from all students in other schools. In health…

函数型时间序列的拟合优度检验及其在 Ornstein–Uhlenbeck 过程中的应用AI 解读

High-frequency financial data can be collected as a sequence of curves over time; for example, as intra-day price, currently one of the topics of greatest interest in finance. The Functional Data Analysis framework provides a suitable tool to extract the information contained in the shape of the daily paths, often unavailable from classical statistical methods. In this paper, a novel goodness-of-fit test for autoregressive Hilbertian (ARH) models, with unknown and general…

稀疏机制变化假说下异质环境中的因果发现AI 解读

Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, however, this assumption is almost always violated due to distribution shifts between environments. Although valuable learning signals can be provided by heterogeneous data from changing distributions, it is also known that learning under arbitrary (adversarial) changes is impossible. Causality provides a useful framework for modeling distribution…

多元连续结局阶梯楔形设计的功效分析AI 解读

Multivariate outcomes are common in pragmatic cluster randomized trials. While sample size calculation procedures for multivariate outcomes exist under parallel assignment, none have been developed for a stepped wedge design. In this article, we present computationally efficient power and sample size procedures for stepped wedge cluster randomized trials (SW-CRTs) with multivariate outcomes that differentiate the within-period and between-period intracluster correlation…

多值处理观察性研究中的目标学习：抗精神病药物治疗安全性评估AI 解读

We investigate estimation of causal effects of multiple competing (multi-valued) treatments in the absence of randomization. Our work is motivated by an intention-to-treat study of the relative cardiometabolic risk of assignment to one of six commonly prescribed antipsychotic drugs in a cohort of nearly 39,000 adults with serious mental illnesses. Doubly-robust estimators, such as targeted minimum loss-based estimation (TMLE), require correct specification of either the…

使用目标试验框架识别并减少多队列研究中的偏差AI 解读

Longitudinal cohort studies, which follow a group of individuals over time, provide the opportunity to examine causal effects of complex exposures on long-term health outcomes. Utilizing data from multiple cohorts has the potential to add further benefit by improving precision of estimates through data pooling and by allowing examination of effect heterogeneity through replication of analyses across cohorts. However, the interpretation of findings can be complicated by…

重尾噪声下的稳健矩阵补全AI 解读

This paper studies low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incomplete noisy entries. Though the matrix completion problem has attracted much attention in the past decade, there is still lack of theoretical understanding when the observations are contaminated by heavy-tailed noises. Prior theory falls short of explaining the empirical results and is…

协同双重机器学习AI 解读

Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with high-dimensional covariates, under the assumption of a partially linear model. The idea is to first fit on a subset of the samples two non-linear predictive models, one for the continuous outcome of interest and one for the observed treatment, and then to estimate a linear coefficient for the…

结合多个实验与多个结局高效估计异质性处理效应AI 解读

Learning heterogeneous treatment effects (HTEs) is an important problem across many fields. Most existing methods consider the setting with a single treatment arm and a single outcome metric. However, in many real world domains, experiments are run consistently - for example, in internet companies, A/B tests are run every day to measure the impacts of potential changes across many different metrics of interest. We show that even if an analyst cares only about the HTEs in one…

时间序列分析与预测中的 Hankel 低秩逼近和补全综述AI 解读

In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting. We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper closes with some expository examples.

高频观测下常数漂移分数布朗运动的拟似然分析AI 解读

Consider an estimation of the Hurst parameter $H\in(0,1)$ and the volatility parameter $σ>0$ for a fractional Brownian motion with a drift term under high-frequency observations with a finite time interval. In the present paper, we propose a consistent estimator of the parameter $θ=(H,σ)$ combining the ideas of a quasi-likelihood function based on a local Gaussian approximation of a high-frequently observed time series and its frequency-domain approximation. Moreover, we…

官方外贸统计的重构攻击：巴西案例AI 解读

In this paper we describe, formalize, implement, and experimentally evaluate a novel transaction re-identification attack against official foreign-trade statistics releases in Brazil. The attack's goal is to re-identify the importers of foreign-trade transactions (by revealing the identity of the company performing that transaction), which consequently violates those importers' fiscal secrecy (by revealing sensitive information: the value and volume of traded goods). We…

主分层存在混杂时的因果效应识别与估计AI 解读

The principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation-by-death problems. The causal effects within principal strata which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. Analyses of principal causal effects from observed data in the literature mostly rely…

资产定价中的模型误设与弱识别AI 解读

The widespread co-existence of misspecification and weak identification in asset pricing has led to an overstated performance of risk factors. Because the conventional Fama and MacBeth (1973) methodology is jeopardized by misspecification and weak identification, we infer risk premia by using a double robust Lagrange multiplier test that remains reliable in the presence of these two empirically relevant issues. Moreover, we show how the identification, and the resulting…

欧盟经济周期同步性：软聚类与小波分解的区域行业视角AI 解读

This paper elaborates on the sectoral-regional view of the business cycle synchronization in the EU -- a necessary condition for the optimal currency area. We argue that complete and tidy clustering of the data improves the decision maker's understanding of the business cycle and, by extension, the quality of economic decisions. We define the business cycles by applying a wavelet approach to drift-adjusted gross value added data spanning over 2000Q1 to 2021Q2. For the…