每日简报

连续时间非线性自回归的偏误校正AI 解读

We study how to identify a class of continuous-time nonlinear systems defined by an ordinary differential equation affine in the unknown parameter. We define a notion of asymptotic consistency as $(n, h) \to (\infty, 0)$, and we achieve it using a family of direct methods where the first step is differentiating a noisy time series and the second step is a plug-in linear estimator. The first step, differentiation, is a signal processing adaptation of the nonparametric…

利用神经网络函数估计非线性自回归过程的变点AI 解读

In this paper, we propose a new test for the detection of a change in a non-linear (auto-)regressive time series as well as a corresponding estimator for the unknown time point of the change. To this end, we consider an at-most-one-change model and approximate the unknown (auto-)regression function by a neuronal network with one hidden layer. It is shown that the test has asymptotic power one for a wide range of alternatives not restricted to changes in the mean of the time…

替代 ARDL？用于结构变化与非对称预测的 NSB-ARDL 模型AI 解读

This paper introduces the NSB-ARDL (Nonlinear Structural Break Autoregressive Distributed Lag) model, a novel econometric framework designed to capture asymmetric and nonlinear dynamics in macroeconomic time series. Traditional ARDL models, while widely used for estimating short- and long-run relationships, rely on assumptions of linearity and symmetry that may overlook critical structural features in real-world data. The NSB-ARDL model overcomes these limitations by…

点过程时间序列的自相关函数AI 解读

This article introduces autocorrelograms for time series of point processes. Such time series usually arise when a longer temporal or spatio-temporal point process is sliced into smaller time units; for example, when an annual process is sliced into 365 daily replications. We assume the point processes follow a doubly-stochastic Poisson model with log-Gaussian intensity functions. The proposed autocorrelograms are computationally simple and based on binning. The asymptotic…

因子增广回归的稳健检验及其在新 EA-MD-QD 数据集中的应用AI 解读

We present four novel tests of equal predictive accuracy and encompassing à la Pitarakis (2023, 2025) for factor-augmented regressions, where factors are estimated using cross-section averages (CAs) of grouped series. Our inferential theory is asymptotically normal and robust to an overspecification of the number of factors. Our tests are empirically relevant as they accommodate for different degrees of predictor persistence and remain invariant to the location of structural…

高维 VAR 模型中的贝叶斯收缩：比较研究AI 解读

High-dimensional vector autoregressive (VAR) models offer a versatile framework for multivariate time series analysis, yet face critical challenges from over-parameterization and uncertain lag order. In this paper, we systematically compare three Bayesian shrinkage priors (horseshoe, lasso, and normal) and two frequentist regularization approaches (ridge and nonparametric shrinkage) under three carefully crafted simulation scenarios. These scenarios encompass (i) overfitting…

高维重尾局部平稳时间序列的稀疏学习：集中与预言不等式AI 解读

Sparse learning is ubiquitous in many machine learning tasks. It aims to regularize the goodness-of-fit objective by adding a penalty term to encode structural constraints on the model parameters. In this paper, we develop a flexible sparse learning framework tailored to high-dimensional heavy-tailed locally stationary time series (LSTS). The data-generating mechanism incorporates a regression function that changes smoothly over time and is observed under noise belonging to…

局部平稳函数型时间序列的 Wasserstein 距离界AI 解读

Functional time series (FTS) extend traditional methodologies to accommodate data observed as functions/curves. A significant challenge in FTS consists of accurately capturing the time-dependence structure, especially with the presence of time-varying covariates. When analyzing time series with time-varying statistical properties, locally stationary time series (LSTS) provide a robust framework that allows smooth changes in mean and variance over time. This work investigates…

基于单位 Lindley 分布的单位区间时间序列 GARMA 框架及可再生能源应用AI 解读

The Unit-Lindley is a one-parameter family of distributions in $(0,1)$ obtained from an appropriate transformation of the Lindley distribution. In this work, we introduce a class of dynamical time series models for continuous random variables taking values in $(0,1)$ based on the Unit-Lindley distribution. The models pertaining to the proposed class are observation-driven ones for which, conditionally on a set of covariates, the random component is modeled by a Unit-Lindley…

函数型线性投影与脉冲响应分析AI 解读

This paper proposes econometric methods for studying how economic variables respond to function-valued shocks. Our methods are developed based on linear projection estimation of predictive regression models with a function-valued predictor and other control variables. We show that the linear projection coefficient associated with the functional variable allows for the impulse response interpretation in a functional structural vector autoregressive model under a certain…