每日简报

基于稳健倒谱系数的平稳时间序列判别分析AI 解读

Time series analysis is crucial in fields like finance, economics, environmental science, and biomedical engineering, aiding in forecasting, pattern identification, and understanding underlying mechanisms. While traditional time-domain methods focus on trends and seasonality, they often miss periodicities better captured in the frequency domain. Analyzing time series in the frequency domain uncovers spectral properties, offering deeper insights into underlying processes,…

多变量计数时间序列分析的潜在空间模型AI 解读

Motivated by a dataset of burglaries in Chicago, USA, we introduce a novel framework to analyze time series of count data combining common multivariate time series models with latent position network models. This novel methodology allows us to gain a new latent variable perspective on the crime dataset that we consider, allowing us to disentangle and explain the complex patterns exhibited by the data, while providing a natural time series framework that can be used to make…

部分识别下的稳健贝叶斯处理选择AI 解读

We study a class of binary treatment choice problems with partial identification through the lens of robust (multiple prior) Bayesian analysis. We use a convenient set of prior distributions to derive ex-ante and ex-post robust Bayes decision rules, both for decision makers who can randomize and for decision makers who cannot. Our main messages are as follows: First, ex-ante and ex-post robust Bayes decision rules do not agree in general, whether or not randomized rules are…

仅需两个截面单位的双重差分：民主与增长的新视角AI 解读

Pooled panel analyses often mask heterogeneity in unit-specific treatment effects. This challenge, for example, crops up in studies of the impact of democracy on economic growth, where findings vary substantially due to differences in country composition. To address this challenge, this paper introduces the Temporal Difference-in-Differences (T-DiD) estimator that leverages temporal variation in the data to estimate unit-specific average treatment effects on the treated…

利用多个替代指标估计处理效应：替代得分与替代指数AI 解读

Estimating the long-term effects of treatments is of interest in many fields. A common challenge in estimating such treatment effects is that long-term outcomes are unobserved in the time frame needed to make policy decisions. One approach to overcome this missing data problem is to analyze treatments effects on an intermediate outcome, often called a statistical surrogate, if it satisfies the condition that treatment and outcome are independent conditional on the…

我们遗漏了谁？识别代表性不足人群的系统方法AI 解读

Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our paper addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of…

双向聚类面板模型的固定 b 渐近理论AI 解读

This paper studies a cluster robust variance estimator proposed by Chiang, Hansen and Sasaki (2024) for linear panels. First, we show algebraically that this variance estimator (CHS estimator, hereafter) is a linear combination of three common variance estimators: the one-way unit cluster estimator, the "HAC of averages" estimator, and the "average of HACs" estimator. Based on this finding, we obtain a fixed-$b$ asymptotic result for the CHS estimator and corresponding test…

非可交换数据下的分割保形预测AI 解读

Split conformal prediction (CP) is arguably the most popular CP method for uncertainty quantification, enjoying both academic interest and widespread deployment. However, the original theoretical analysis of split CP makes the crucial assumption of data exchangeability, which hinders many real-world applications. In this paper, we present a novel theoretical framework based on concentration inequalities and decoupling properties of the data, proving that split CP remains…

因果发现之后的有效推断AI 解读

Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to "double dipping," invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery…

重新研究含滞后因变量的面板离散选择模型AI 解读

This paper revisits the identification and estimation of a class of semiparametric (distribution-free) panel data binary choice models with lagged dependent variables, exogenous covariates, and entity fixed effects. We provide a novel identification strategy, using an "identification at infinity" argument. In contrast with the celebrated Honore and Kyriazidou (2000), our method permits time trends of any form and does not suffer from the "curse of dimensionality". We propose…

不完美参照检测下诊断性能的部分识别AI 解读

Diagnostic tests are almost never perfect. Studies quantifying their performance use knowledge of the true health status, measured with a reference diagnostic test. Researchers commonly assume that the reference test is perfect, which is often not the case in practice. When the assumption fails, conventional studies identify "apparent" performance or performance with respect to the reference, but not true performance. This paper provides the smallest possible bounds on the…

ρ-GNF：利用 Copula 与归一化流分析未观测混杂的敏感性AI 解读

We propose a novel sensitivity analysis to unobserved confounding in observational studies using copulas and normalizing flows. Using the idea of interventional equivalence of structural causal models, we develop $ρ$-GNF ($ρ$-graphical normalizing flow), where $ρ{\in}[-1,+1]$ is a bounded sensitivity parameter. This parameter represents the back-door non-causal association due to unobserved confounding, and which is encoded with a Gaussian copula. In other words, the $ρ$-GNF…

考虑数据隐私的观察性与实验数据融合因果推断AI 解读

Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive information might be tolerable to organizations that house confidential data. In these cases, organizations can employ data privacy…

重新审视日前电价预测：简单模型与潜在成本节约AI 解读

Accurate day-ahead electricity price forecasting is essential for residential welfare, yet current methods often fall short in forecast accuracy. We observe that commonly used time series models struggle to utilize the prior correlation between price and demand-supply, which, we found, can contribute a lot to a reliable electricity price forecaster. Leveraging this prior, we propose a simple piecewise linear model that significantly enhances forecast accuracy by directly…

处理组自助法：量化匹配处理效应估计的不确定性AI 解读

This paper proposes a new non-parametric bootstrap method to quantify the uncertainty of average treatment effect estimate for the treated from matching estimators. More specifically, it seeks to quantify the uncertainty associated with the average treatment effect estimate for the treated by bootstrapping the treatment group only and finding the counterpart control group by pair matching on estimated propensity score without replacement. We demonstrate the validity of this…

异方差与异常值下过度识别线性模型的估计AI 解读

A large degree of overidentification causes severe bias in TSLS. A conventional heuristic rule used to motivate new estimators in this context is approximate bias. This paper formalizes the definition of approximate bias and expands the applicability of approximate bias to various classes of estimators that bridge OLS, TSLS, and Jackknife IV estimators (JIVEs). By evaluating their approximate biases, I propose new approximately unbiased estimators, including UOJIVE1 and…

将匹配固定效应识别为可解释的未观测匹配亲和度AI 解读

We highlight that match fixed effects, represented by the coefficients of interaction terms involving dummy variables for two elements, lack identification without specific restrictions on parameters. Consequently, the coefficients typically reported as relative match fixed effects by statistical software are not interpretable. To address this, we establish normalization conditions that enable identification of match fixed effect parameters as interpretable indicators of…

Mixstyle-Entropy：结合因果干预与扰动的域泛化AI 解读

Despite the considerable advancements achieved by deep neural networks, their performance tends to degenerate when the test environment diverges from the training ones. Domain generalization (DG) solves this issue by learning representations independent of domain-related information, thus facilitating extrapolation to unseen environments. Existing approaches typically focus on formulating tailored training objectives to extract shared features from the source data. However,…

用于多变量面板数据的隐马尔可夫模型AI 解读

While advances continue to be made in model-based clustering, challenges persist in modeling various data types such as panel data. Multivariate panel data present difficulties for clustering algorithms because they are often plagued by missing data and dropouts, presenting issues for estimation algorithms. This research presents a family of hidden Markov models that compensate for the issues that arise in panel data. A modified expectation-maximization algorithm capable of…

从优化视角理解因果推断的实验设计AI 解读

The study of experimental design offers tremendous benefits for answering causal questions across a wide range of applications, including agricultural experiments, clinical trials, industrial experiments, social experiments, and digital experiments. Although valuable in such applications, the costs of experiments often drive experimenters to seek more efficient designs. Recently, experimenters have started to examine such efficiency questions from an optimization…

利用神经网络估计暴露分布变化的因果效应AI 解读

A fundamental task in causal inference is estimating the effect of distribution shift in the treatment variable. We refer to this problem as shift-response function (SRF) estimation. Existing neural network methods for causal inference lack theoretical guarantees and practical implementations for SRF estimation. In this paper, we introduce Targeted Regularization for Exposure Shifts with Neural Networks (TRESNET), a method to estimate SRFs with robustness and efficiency…

计量电价预测模型：从日前预测到中长期预测AI 解读

The recent energy crisis starting in 2021 led to record-high gas, coal, carbon and power prices, with electricity reaching up to 40 times the pre-crisis average. This had dramatic consequences for operational and risk management prompting the need for robust econometric models for mid to long-term electricity price forecasting. After a comprehensive literature analysis, we identify key challenges and address them with novel approaches: 1) Fundamental information is…

矩阵分位数因子模型AI 解读

This paper introduces a matrix quantile factor model for matrix-valued data with low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check loss function with orthogonal rotation constraints. We show that the estimates converge at rate $(\min\{p_1p_2,p_2T,p_1T\})^{-1/2}$ in the average Frobenius norm, where $p_1$, $p_2$ and $T$ are the row dimensionality, column dimensionality and length of the matrix sequence, respectively. This rate…