每日简报

融合黎曼 Zeta 函数与 FPAS 的通胀预测：周期、冲击与模型比较AI 解读

Inflation forecasting is a core socio-economic challenge in modern macroeconomic modeling, especially when cyclical, structural, and shock factors act simultaneously. Traditional systems such as FPAS and ARIMA often struggle with cyclical asymmetry and unexpected fluctuations. This study proposes a hybrid framework (FPAS + $ζ$) that integrates a structural macro model (FPAS) with cyclical components derived from the Riemann zeta function $ζ(1/2 + i t)$. Using Georgia's macro…

基于复合分位数的数据自适应因子模型AI 解读

This paper proposes a data-adaptive factor model (DAFM), a novel framework for extracting common factors that explain the structures of high-dimensional data. DAFM adopts a composite quantile strategy to adaptively capture the full distributional structure of the data, thereby enhancing estimation accuracy and revealing latent patterns that are invisible to conventional factor models. In this paper, we develop a practical algorithm for estimating DAFM by minimizing an…

日内颗粒物粒径数量分布预测：函数型时间序列方法AI 解读

Particulate matter data now include various particle sizes, which often manifest as a collection of curves observed sequentially over time. When considering 51 distinct particle sizes, these curves form a high-dimensional functional time series observed over equally spaced and densely sampled grids. While high dimensionality poses statistical challenges due to the curse of dimensionality, it also offers a rich source of information that enables detailed analysis of temporal…

非参数向量分位数自回归AI 解读

Prediction is a key issue in time series analysis. Just as classical mean regression models, classical autoregressive methods, yielding L$^2$ point-predictions, provide rather poor predictive summaries; a much more informative approach is based on quantile (auto)regression, where the whole distribution of future observations conditional on the past is consistently recovered. Since their introduction by Koenker and Xiao in 2006, autoregressive quantile autoregression methods…

孟加拉国七月革命中镇压与动员的混合方法分析AI 解读

The 2024 July Revolution in Bangladesh represents a landmark event in the study of civil resistance. This study investigates the central paradox of the success of this student-led civilian uprising: how state violence, intended to quell dissent, ultimately fueled the movement's victory. We employ a mixed-methods approach. First, we develop a qualitative narrative of the conflict's timeline to generate specific, testable hypotheses. Then, using a disaggregated, event-level…

利用逐级比率估计对 trawl 过程进行基于模拟的推断AI 解读

The growing availability of large and complex datasets has increased interest in temporal stochastic processes that can capture stylized facts such as marginal skewness, non-Gaussian tails, long memory, and even non-Markovian dynamics. While such models are often easy to simulate from, parameter estimation remains challenging. Simulation-based inference (SBI) offers a promising way forward, but existing methods typically require large training datasets or complex…

面板数据断点检测的加权回归方法AI 解读

New procedures for detecting a change in the cross-sectional mean of panel data are proposed. The procedures rely on estimating nuisance parameters using certain cross-sectional means across panels using a weighted least squares regression. In the case of weak cross-sectional dependence between panels, we show how test statistics can be constructed to have a limit null distribution not depending on any choice of bandwidths typically needed to estimate the long-run variances…

时间序列预测推断：时间依赖下拆分共形预测为何仍然有效？AI 解读

We consider the problem of uncertainty quantification for prediction in a time series: if we use past data to forecast the next time point, can we provide valid prediction intervals around our forecasts? To avoid placing distributional assumptions on the data, in recent years the conformal prediction method has been a popular approach for predictive inference, since it provides distribution-free coverage for any iid or exchangeable data distribution. However, in the time…

即时预测与聚合：欧元区小型经济体为何重要AI 解读

The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro releases and daily news data. Using a panel of 19 Euro area countries, we investigate whether directly nowcasting the Euro area aggregate is better than weighted individual country nowcasts. Our results highlight the importance of the information from small- and medium-sized countries, particularly when…

多变量时间序列软聚类与硬聚类的模糊跳跃模型AI 解读

Statistical jump models have been recently introduced to detect persistent regimes by clustering temporal features and discouraging frequent regime changes. However, they are limited to hard clustering and thereby do not account for uncertainty in state assignments. This work presents an extension of the statistical jump model that incorporates uncertainty estimation in cluster membership. Leveraging the similarities between statistical jump models and the fuzzy c-means…

用于缺失值填补的扩散 Transformer：统计效率与不确定性量化AI 解读

Imputation methods play a critical role in enhancing the quality of practical time-series data, which often suffer from pervasive missing values. Recently, diffusion-based generative imputation methods have demonstrated remarkable success compared to autoregressive and conventional statistical approaches. Despite their empirical success, the theoretical understanding of how well diffusion-based models capture complex spatial and temporal dependencies between the missing…

时空极值的摊销贝叶斯推断：带自回归项的 Copula 因子模型AI 解读

We develop a Bayesian spatio-temporal framework for extreme-value analysis that augments a hierarchical copula model with an autoregressive factor to capture residual temporal dependence in threshold exceedances. The factor can be specified as spatially varying or spatially constant, and the scale parameter incorporates scientifically relevant covariates (e.g., longitude, latitude, altitude), enabling flexible representation of geographic heterogeneity. To avoid the…

复杂系统的推断极限：随机模型何时不可区分AI 解读

Robust inference for stochastic dynamical systems is often hampered by sparse sampling and the absence of closed-form likelihoods. We introduce a Monte Carlo path-inference framework that leverages full-path statistics and bridge processes to deliver reliable parameter estimation and model selection from coarsely sampled time series, without requiring analytical solutions. Crucially, we couple mechanistic stochastic models with their inference procedures to quantify how…

从建构到承诺：叙事对经济增长的影响AI 解读

We study how government-led narratives through mass media evolve from construct, a mechanism for framing expectations, into commitment, a sustainable pillar for growth. We propose the "Narratives-Construct-Commitment (NCC)" framework outlining the mechanism and institutionalization of narratives, and formalize it as a dynamic Bayesian game. Using the Innovation-Driven Development Strategy (2016) as a case study, we identify the narrative shock from high-frequency financial…

非平稳与误差污染下的函数型回归：气候变化经济影响应用AI 解读

This paper studies a regression model with functional dependent and explanatory variables, both of which exhibit nonstationary dynamics. The model assumes that the nonstationary stochastic trends of the dependent variable are explained by those of the explanatory variables, and hence that there exists a stable long-run relationship between the two variables despite their nonstationary behavior. We also assume that the functional observations may be error-contaminated. We…

AR(1) 模型的局部投影残差自助法AI 解读

This paper proposes a local projection residual bootstrap method to construct confidence intervals for impulse response coefficients of AR(1) models. Our bootstrap method is based on the local projection (LP) approach and involves a residual bootstrap procedure applied to AR(1) models. We present theoretical results for our bootstrap method and proposed confidence intervals. First, we prove the uniform consistency of the LP-residual bootstrap over a large class of AR(1)…

非中心化随机波动率下结构向量自回归的部分识别AI 解读

We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, with strong shrinkage and heavy tails -- unlike the common centred parameterization. This feature makes it well suited for…

基于 Hermite 多项式的非线性预测误差方差分解AI 解读

A novel approach to Forecast Error Variance Decompositions (FEVD) in nonlinear Structural Vector Autoregressive models with Gaussian innovations is proposed, called the Hermite FEVD (HFEVD). This method employs a Hermite polynomial expansion to approximate the future trajectory of a nonlinear process. The orthogonality of Hermite polynomials under the Gaussian density facilitates the construction of the decomposition, providing a separation of shock effects by time horizon,…

流形上函数型时间序列长程依赖的谱域检验AI 解读

A statistical hypothesis test for long range dependence (LRD) is formulated in the spectral domain for functional time series in manifolds. The elements of the spectral density operator family are assumed to be invariant with respect to the group of isometries of the manifold. The proposed test statistic is based on the weighted periodogram operator. A Central Limit Theorem is derived to obtain the asymptotic Gaussian distribution of the proposed test statistic operator…

时变参数的贝叶斯自回归在线变点检测AI 解读

Change points in real-world systems mark significant regime shifts in system dynamics, possibly triggered by exogenous or endogenous factors. These points define regimes for the time evolution of the system and are crucial for understanding transitions in financial, economic, social, environmental, and technological contexts. Building upon the Bayesian approach introduced in \cite{c:07}, we devise a new method for online change point detection in the mean of a univariate…

弱因子下因子增强回归模型的偏差修正AI 解读

In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the $r$ factors extracted from a large number of $N$ variables with $T$ observations. In particular, we consider general weak latent factor models with $r$ signal eigenvalues that may diverge at different rates, $N^{α_{k}}$, $0<α_{k}\leq 1$, $k=1,\dots,r$. In the existing literature, the bias has been derived using an approximation for the…