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We study structural changes in multivariate time series by estimating and comparing stationary graphs of macroeconomic time series before and after an economic crisis, such as the Great Recession. Building on a latent time series framework called Orthogonally-rotated Univariate Time-series (OUT), we propose a shared-parameter framework-the spOUT autoregressive model (spOUTAR)-that jointly models two related multivariate time series and enables coherent Bayesian estimation of…

带自回归误差的线性回归在线变点检测：流式变分贝叶斯方法AI 解读

Large-scale streaming time-series regression requires methods that can detect structural changes while accounting for serially correlated errors and quantifying uncertainty about the current regime. We propose a streaming variational Bayes method for linear regression with autoregressive errors that combines online changepoint detection with sequential posterior inference. The proposed method processes observations in chronological mini-batches and uses a finite-mixture…

重叠多步预测的贝叶斯协调：跨预测起点一致性的后处理框架AI 解读

Multi-step forecasting systems, including modern artificial intelligence (AI) models, often issue overlapping predictions for the same future time from different forecast origins. These predictions can disagree even when produced by the same model, a phenomenon we call cross-origin incoherence. We propose a Bayesian reconciliation framework that treats overlapping forecasts as noisy and potentially biased measurements of a common latent trajectory. The method operates as a…

We tackle the issue of producing point, sign, and density forecasts for the monthly real price of carbon in the European Union Emissions Trading System (EU ETS). We show that Bayesian Vector Autoregressive (BVAR) models augmented with factors based on macroeconomic and financial variables yield improvements in point and density forecasting performance, with the largest point-forecast gains emerging at intermediate and longer horizons. In particular, the one-factor BVAR model…

欧元区货币政策传导是否存在不对称性？AI 解读

We answer the question posed in the title with a nonlinear mixed-frequency vector autoregression, estimated with Bayesian additive regression trees. The model combines monthly macro-financial variables with quarterly bank lending survey data, and identifies the dynamic responses from high-frequency policy surprises. Sign asymmetry dominates; a tightening produces monetary policy transmission mostly in line with the theoretical predictions, whereas easing of any size produces…

概率季节性调整AI 解读

Seasonal adjustment is fundamental to economic analysis, but uncertain because seasonal components are inherently latent. This article introduces a probabilistic model discovery method that decomposes a time series into seasonal and nonseasonal components. The method returns a posterior distribution over the structure and parameters of a seasonal component. In simulation studies, the method can improve point forecasts, interval predictions, and recovery of seasonal…

用收缩先验建模动态相关矩阵AI 解读

Estimating time-varying correlation matrices is challenging because existing methods may adapt slowly to structural changes, impose insufficient regularization, or produce diffuse posterior uncertainty. In moderate dimensions, an additional difficulty is summarizing the estimated evolving dependence structure for downstream decision-making tasks. We propose a Bayesian approach based on a low-rank factor representation, with latent states evolving under a dynamic shrinkage…

Hájek 卷积定理的二阶扩展及统计应用AI 解读

For a class of regular estimators, Hájek, in his celebrated ``Convolution Theorem,'' showed that the asymptotic distribution of a regular estimator is the convolution of the distribution of an efficient estimator and some residual distribution. This result constitutes the foundation of the concept of asymptotic efficiency of regular estimators. In this paper, we provide a second-order version of that classical result. Introducing a class of second-order regular estimators…

图结构时间序列的滤波共形预测椭球AI 解读

Joint prediction sets for multivariate time series should control a single event while adapting to cross-coordinate dependence. We study filtered conformal ellipsoids: a frozen state-space filter emits a one-step predictive mean and covariance, and split-conformal calibration is applied to the resulting Mahalanobis scores. The filter is used to choose the ellipsoid shape; conformal calibration chooses the scalar radius, so the construction benefits from a learned predictive…

利用最佳顺序得分搜索学习时间序列因果结构AI 解读

Causal structure learning from observational data is central to many scientific and policy domains, but the time series setting common to many disciplines poses several challenges due to temporal dependence. In this paper we focus on score-based causal discovery for multivariate time series and introduce TS-BOSS, a time series extension of the recently proposed Best Order Score Search (BOSS) (Andrews et al. 2023). TS-BOSS performs a permutation-based search over dynamic…

具有 Cholesky 随机波动率的大型 VAR：贝叶斯推断、在线预测与模型选择AI 解读

We consider $K$-dimensional Bayesian vector autoregressions (BVARs) with Cholesky stochastic volatility (SV), in which the innovation covariance matrix is a lower-triangular linear transform of $K$ independent univariate SV processes. Such models are widely used in empirical macroeconomics to capture time-varying uncertainty and improve forecast accuracy, but the cost of posterior simulation is the binding constraint on the size of the system, and a major bottleneck for…

面向二元、删失与计数变量的大型贝叶斯VARAI 解读

We extend the vector autoregression (VAR) to model binary, censored, count and unrestricted variables jointly, treating missing observations of any type in the same way. Each restricted observation is linked to a latent Gaussian variable, so that the standard machinery for VARs, such as shrinkage priors, stochastic volatility and outlier components, applies directly to the VAR for the latent variables. The latent variables have a truncated normal conditional distribution…

长平稳时间序列的频域分治 MCMCAI 解读

The temporal dependence inherent in time series models poses a fundamental challenge for distributed Bayesian inference, as it precludes embarrassingly parallel algorithms based on naive independence assumptions in the time domain. We propose a frequency-domain framework for scalable Bayesian inference in stationary time series that exploits the asymptotic independence underlying the Whittle likelihood. To exploit parallel computing resources, we develop a distributed fast…

条件线性的矩阵正态状态空间模型AI 解读

We develop a class of linear state space models for matrix-valued time series data where the state is a latent matrix normal process. We derive matrix versions of the Kalman filter, log-likelihood, and smoother enabling estimation of the latent state matrix as well as the model's parameters. To conduct Bayesian inference, we provide algorithms that draw from the joint posterior distribution of the latent state matrices conditional on the observed data and parameters. We…

伪增量检验：利用自然发生的干预衡量广告增量效应AI 解读

We develop a method for measuring the incremental effect of advertising when randomized exper- iments are unavailable. Firms generate abrupt interventions in their own marketing as a byproduct of operations: budgets are cut, channels launch, programs pause. We propose a two-stage proce- dure that treats these events as quasi-experiments. The first stage discovers and dates interventions by exact Bayesian run-length inference on marketing activity series. The second stage…

用于因果推断与模型发现的连续时间集合 Kalman–Bucy 平滑器AI 解读

Data assimilation (DA) integrates observational information with model predictions to improve state estimation in complex systems. While filtering provides the basis for online forecasts by using only past and present observations, it can exhibit delays and biases when the underlying dynamics evolve rapidly or undergo regime transitions. Smoothing, which additionally incorporates future observations, provides a natural pipeline for hindcasting and reanalysis that yields an…

超越点预测：时间序列与时空数据概率预测综述AI 解读

Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across temporal and spatiotemporal forecasting, statistical modeling, machine learning, and deep generative modeling. This survey develops a unified perspective by organizing probabilistic forecasting methods according to where and how uncertainty is introduced into the forecasting pipeline. Our taxonomy connects model-agnostic…

具有结构化收缩与持续性先验的贝叶斯广义网络自回归模型AI 解读

We propose a Bayesian generalized network autoregressive (BGNAR) model for multivariate time series whose component series are associated with the nodes of a known network. The proposed framework combines the parsimonious network structure of the generalized network autoregressive (GNAR) model with structured shrinkage and persistence priors adapted from Bayesian vector autoregressive (BVAR) modeling. We adapt Minnesota-type shrinkage to both own-lag and network-lag…

超越总量 VAR：异质性主体新凯恩斯模型的贝叶斯基准AI 解读

Heterogeneous-agent New Keynesian (HANK) models characterize how entire cross-sectional distributions respond to structural shocks. Traditional representative-agent models are routinely disciplined by impulse responses from aggregate vector autoregressions (VARs). HANK models have no comparable established empirical benchmark because they make predictions not only about aggregates, but also about distributions of micro-level data. We propose a Bayesian benchmark that jointly…

时间序列概率重抽样的狄利克雷分位数自助法AI 解读

This work presents DPQBootstrap, a probabilistic resampling method for time series based on a Dirichlet quantile bootstrap and rank-based temporal reconstruction. The method aims to generate pseudo-series that preserve the main characteristics of the original series, including variability, quantiles, extreme values, and part of its temporal dependence. A simulation study compares DPQBootstrap with the Maximum Entropy Bootstrap implemented in the meboot package across eight…

具有平稳有向无环图结构的高维时间序列贝叶斯推断AI 解读

In multivariate time series analysis, understanding the underlying causal relationships among variables is often of interest for various applications. Directed acyclic graphs (DAGs) provide a powerful framework for representing causal dependencies. This paper proposes a novel Bayesian approach for modeling multivariate time series where conditional independencies and causal structure are encoded by a DAG. The proposed model allows structural properties such as stationarity…

用于非线性时间序列的贝叶斯分位数深度回声状态网络AI 解读

Conditional quantiles are central to asymmetric decision losses, tail-risk assessment, and interval forecasts, but Bayesian quantile regression for nonlinear time series can be difficult when the temporal feature vector is high-dimensional. We develop the quantile deep echo state network (Q-DESN), a Bayesian quantile regression model conditional on fixed features generated by a deep echo state network. Conditional on a specified reservoir construction and feature map,…

因果—非因果状态空间模型与金融泡沫建模AI 解读

In this paper, we study causal non-causal state space models to model time series characterised by a local explosive increase followed by a sharp decrease such as stock prices. To motivate the use of causal non-causal state space models, we show that the causal non-causal convolution autoregressive model introduced by Gourieroux and Zakoian (2017) can be consistent with the rational expectations stock price model. As in a causal state space model, a central question is how…

厚尾与随机波动率下规范化向量自回归预测软件的设计AI 解读

We present a suite of R packages for macroeconomic forecasting that leverages advanced Bayesian, structural, multivariate, dynamic, hierarchical, non-linear, and non-Gaussian models. The suite enables both structural and predictive analyses, and is adapted to time series data across various types, dimensions, and sampling frequencies. Each additional feature increases computational complexity. To address this challenge, our software design incorporates a carefully curated…