每日简报

DoTime：干预与反事实时间序列的合成基准生成器AI 解读

Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science. We introduce \textbf{DoTime}, an open, scalable, and theoretically grounded generator of multivariate temporal structural causal models (TSCMs) with interventions, released as the \code{dotime} PyPI package together…

用 BAND 缓解维度灾难：高维非参数分布估计AI 解读

Minimax-optimal rates for multivariate distribution estimation are known to suffer from the curse of dimensionality. We propose a sparse Bayesian network approach in which each conditional probability is estimated using sparsity-aware conditional mean methods. The resulting estimator, \textit{BAyesian Network Distribution regression} (BAND), handles mixed data types in high-dimensional time series and achieves polynomial total variation convergence rates while allowing the…

上确界型统计量的实质性变化自归一化推断AI 解读

We develop a selfnormalized approach to inference for relevant changes in functional time series measured by the supremum norm. The main difficulty is that the supremum norm is not Hadamard differentiable, so standard projection-based selfnormalization does not apply and the limiting distribution may depend on the geometry of the extremal set and the long-run covariance structure. We address this problem by replacing the supremum norm with a smooth log-sum-exp approximation…

通过预过滤筛式自助法校正函数型时间序列长记忆估计偏差AI 解读

We investigate a bias correction procedure based on sieve bootstrapping to estimate the long-memory parameter d in stationary or nonstationary fractionally integrated processes. The resampling method implements a sieve bootstrap method on data prefiltered by a preliminary estimate of the long-memory parameter. For the initial estimate, we recommend the local polynomial Whittle with noise (LPWN) estimator in Frederiksen et al. (2012) to reduce bias, especially in the presence…

流形值时间序列的黎曼因子模型AI 解读

We propose a Riemannian factor model (RFM), a novel framework for analyzing potentially high-dimensional time series data observed on Riemannian manifolds. Such time series are encountered in various applications, including economics, finance, medical imaging, and genomics and microbiome research. The proposed model is geometry-aware and accounts for the inherent nonlinearity in the data. In a high-dimensional asymptotic regime, where the manifold dimension is allowed to…

大规模贝叶斯 VAR 的条件投影方法AI 解读

We develop fast methods for conditional forecasting and structural scenario analysis with high-dimensional Bayesian vector autoregressions (VARs). Our general framework features a factor structure on the reduced-form errors, which enables fast and order-invariant equation-by-equation estimation; suitably identified factors admit a structural interpretation. The scenarios are defined through separate distributional restrictions on observables, structural shocks and…

国际溢出的结构矩阵自回归框架AI 解读

Understanding how macroeconomic shocks propagate across countries requires structural models that can jointly identify country-specific shocks and their international transmission. Yet extending structural vector autoregressions (SVARs) to large multi-country systems is challenging due to rapidly increasing dimensionality, computational costs, and the proliferation of identifying restrictions. This paper develops a Bayesian Structural Matrix Autoregression (BSMAR) framework…

含 p 个参数的动态模型可由 2p+1 个随机特征识别AI 解读

A foundational principle in nonlinear dynamics is that the structure of a dynamical system can be recovered from a small number of generic measurements or coordinates. We develop an analogous principle for the identification of dynamic models for time series with noise, which builds on previous identification results for noiseless dynamical systems. The noise is allowed to be non-iid, non-Gaussian, and dependent on the state. Our results cover noisily observed differential…

数据稀缺环境中以全球因子识别地方冲击：意大利区域财政乘数应用AI 解读

We propose a novel econometric methodology for Structural Vector Autoregressions with external instruments (`proxy-SVARs' or `SVAR-IVs') in panel data characterized by strong cross-sectional dependence, dynamic heterogeneity, and limited availability of direct external instruments for the shocks of interest. For each unit, we specify a Factor-Augmented proxy-SVAR (`proxy-FA-SVAR') that incorporates factors summarizing cross-sectional information from the non-policy variables…

时间序列贝叶斯谱建模中的频率选择及可穿戴设备应用AI 解读

This paper introduces a Bayesian spike-and-slab framework for spectral analysis of time series data. The proposed method combines frequency selection and dimensionality reduction with a refined grid of candidate frequencies, enabling high-resolution recovery of oscillatory components while promoting sparsity through a structured spike-and-slab prior. A stochastic search algorithm efficiently explores the posterior space, yielding posterior inclusion probabilities that…

预测协调与成分数据分析：意大利分死因死亡年龄研究AI 解读

We introduce a novel approach that integrates compositional data analysis (CoDA) in the forecasting of hierarchical and grouped time series and apply it to improve mortality forecasts. By leveraging the inherent decomposition of mortality data by cause of death and sex, our framework addresses key challenges in mortality forecasting, ensuring internal coherence across different levels of aggregation while maintaining high predictive accuracy. Our empirical results on Italian…

关键矿产投资中的能源与碳排放溢出：动态连通性方法AI 解读

Design/methodology/approach A time-varying parameter vector autoregression (TVP-VAR) model is employed to quantify dynamic connectedness and directional volatility spillovers using daily data from May 1, 2013, to May 2, 2023. The study isolates the impact of extreme events by splitting the data into pre- and post-COVID-19 samples based on the February 2020 stock market crash. Purpose This paper examines the daily financial risk spillovers associated with investing in…

结构向量自回归的全局识别AI 解读

In a landmark contribution to the structural vector autoregression (SVARs) literature, Rubio-Ramirez, Waggoner, and Zha (2010, `Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,' Review of Economic Studies) shows a necessary and sufficient condition for equality restrictions to globally identify the structural parameters of a SVAR. The simplest form of the necessary and sufficient condition shown in Theorem 7 of Rubio-Ramirez et al…

非参数时间序列回归中跳跃的高维推断AI 解读

We study simultaneous inference on jumps in the conditional mean functions of a high-dimensional collection of heterogeneous nonparametric time series, where the number of series may exceed the sample size and the data may exhibit strong cross-sectional dependence. The jump depends on one specific covariate, and we allow the regression function to vary with additional latent variables. We propose two uniform tests: one for the existence of jumps and one for their homogeneity…

随机时间序列回归模型中结构断点的非参数检测AI 解读

We propose a novel nonparametric test to detect structural breaks in the conditional mean and/or variance of a time series. Our method does not assume any specific parametric form for the dependence structure of the regressor, the time series model, or the distribution of the noise. This flexibility allows our algorithm to be applicable to a wide range of framework. We further apply the proposed test to accurately localize the changepoints and establish theoretical…

复杂 SVAR 的依赖野生自助推断：波动率工具变量应用AI 解读

We propose a dependent wild bootstrap method based on local projections for computing the joint asymptotic distribution of parameter estimates in structural vector autoregression models. This procedure can be applied to the raw data in levels without pretesting while remaining robust to unit roots, cointegration, polynomial trends, and conditionally heteroscedastic shocks in a general form. We show how knowledge of the joint asymptotic distribution in persistent data setups…

非高斯输入下 Volterra 互相关识别的闭式偏度惩罚AI 解读

The monomial parameterization of finite-memory Volterra identification is ill-conditioned under non-Gaussian input, and the Wiener--Hermite expansion removes this ill-conditioning only for Gaussian white-noise input. We construct the distribution-matched Volterra--Wiener--Kunchenko (VWK) basis by oriented Gram--Schmidt orthogonalization of monomials in $L^2(P)$ and use it as an arbitrary-polynomial-chaos coordinate system for finite-memory Volterra identification from data,…

时间序列回归的稀疏树状聚合AI 解读

High-dimensional time series regressions are often regularized to produce sparse coefficients. We show that temporal aggregation provides a powerful alternative to reduce dimensionality in high-order autoregressions and mixed-frequency regressions. To this end, we propose StarTime (Sparse Tree-based Aggregation for Time Series), a convex penalization method that uses a temporal tree to arrange lags hierarchically from high to low frequency. StarTime then flexibly selects…

利用均值偏移惩罚检测状态空间模型中的异常值AI 解读

State-space models (SSMs) provide a flexible framework for modelling time series data, but their reliance on Gaussian error assumptions makes them highly sensitive to outliers. We propose a robust estimation method, ROAMS, that mitigates the influence of additive outliers by introducing shift parameters at each timepoint in the observation equation of the SSM. These parameters allow the model to attribute non-zero shifts to outliers while leaving clean observations…

使用 Nash–Sutcliffe 损失进行学习AI 解读

The Nash-Sutcliffe efficiency ($\text{NSE}$) is a widely used, positively oriented relative measure for evaluating forecasts across multiple time series. However, it lacks a decision-theoretic foundation for this purpose. To address this, we examine its negatively oriented counterpart, which we refer to as Nash-Sutcliffe loss, defined as $L_{\text{NS}} = 1 - \text{NSE}$. We prove that $L_{\text{NS}}$ is strictly consistent for an elicitable and identifiable multi-dimensional…

样本个体数为一时的实验设计AI 解读

N-of-1 trials, or time-series experiments, are widely used in clinical research and online platforms. Yet the theoretically optimal design for estimating many treatment effects remains unclear. We propose a simple Markovian framework for experimental design in which the treatment assignment process is governed by possibly time-varying transition matrices. This formulation encompasses many existing N-of-1 designs and provides a principled way to control temporal dependence in…

离散卡方方法与厄尔尼诺时间序列中的太阳强迫：太平洋辐射计解释AI 解读

Discrete Chi-square Method (DCM) can detect multiple signals superimposed on an arbitrary trend. DCM's backbone is Gauss-Markov theorem that Least Squares (LS) is the best unbiased estimator for linear regression models. DCM is robust because it computes numerous linear model LS fits. Discrete Fourier Transform and other frequency-domain methods have many application limitations. None of those limitations constrains DCM. Fisher-test provides signal significances and…

极端事件中的格兰杰因果关系AI 解读

We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series. Granger causality plays a pivotal role in uncovering directional relationships among time-varying variables. While this notion gains heightened importance during extreme and highly volatile periods, state-of-the-art methods primarily focus on causality within the body of the distribution, often overlooking causal mechanisms…

均值需估计时 L-mixing 时间序列经典谱估计量的非渐近分析AI 解读

Spectral estimation is an important tool in time series analysis, with applications including economics, astronomy, and climatology. The asymptotic theory for non-parametric estimation is well-known but the development of non-asymptotic theory is still ongoing. Our recent work obtained the first non-asymptotic error bounds on the Bartlett and Welch methods with restrictive assumptions. In this work, we derive non-asymptotic error bounds for both Bartlett and Welch estimators…