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迭代移动平均的扩展AI 解读

This work introduces an extension of the iterated moving average filter, called the Extended Kolmogorov-Zurbenko (EKZ) filter for time series and spatio-temporal analysis. The iterated application of a central simple moving average (SMA) filter, also known as a Kolmogorov-Zurbenko (KZ) filter, is a low-pass filter defined by the length of the moving average window and the number of iterations. These two arguments determine the filter properties such as the energy transfer…

中等高维下稳健估计 Hurst 分布的谱聚类型算法AI 解读

Scale invariance (fractality) is a prominent feature of the large-scale behavior of many stochastic systems. In this work, we construct an algorithm for the statistical identification of the Hurst distribution (in particular, the scaling exponents) undergirding a high-dimensional fractal system. The algorithm is based on wavelet random matrices, modified spectral clustering and a model selection step for picking the value of the clustering precision hyperparameter. In a…

具有内生性与半长记忆的非参数协整回归函数AI 解读

This article develops nonparametric cointegrating regression models with endogeneity and semi-long memory. We assume that semi-long memory is produced in the regressor process by tempering of random shock coefficients. The fundamental properties of long memory processes are thus retained in the regressor process. Nonparametric nonlinear cointegrating regressions with serially dependent errors and endogenous regressors driven by long memory innovations have been considered in…

分布滞后模型的贝叶斯变量选择：二元分位数与计数回归AI 解读

Distributed Lag Models (DLMs) and similar regression approaches such as MIDAS have been used for many decades in econometrics and more recently to investigate how poor air quality adversely affects human health. In this paper we describe how to expand the utility of these models for Bayesian inference by leveraging latent variables. In particular we explain how to perform binary regression to better handle imbalanced data, how to incorporate negative binomial regression, and…

基于连续时间观测的遍历双 Heston 模型：渐近性质与漂移参数估计AI 解读

The double Heston model is one of the most popular option pricing models in financial theory. It is applied to several issues such that risk management and volatility surface calibration. This paper deals with the problem of global parameter estimations in this model. Our main stochastic results are about the stationarity and the ergodicity of the double Heston process. The statistical part of this paper is about the maximum likelihood and the conditional least squares…

使用 R 包 bsvarSIGNs 分析带符号、零值与叙事限制的贝叶斯结构 VARAI 解读

The R package bsvarSIGNs implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions identified by sign, zero, and narrative restrictions. It offers fast and efficient estimation thanks to the deployment of frontier econometric and numerical techniques and algorithms written in C++. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors. The…

高维扩散过程的一致支撑恢复AI 解读

Statistical inference for stochastic processes has advanced significantly due to applications in diverse fields, but challenges remain in high-dimensional settings where parameters are allowed to grow with the sample size. This paper analyzes a d-dimensional ergodic diffusion process under sparsity constraints, focusing on the adaptive Lasso estimator, which improves variable selection and bias over the standard Lasso. We derive conditions under which the adaptive Lasso…

泡沫建模与标记：随机非线性自回归方法AI 解读

Economic and financial time series can feature locally explosive behavior when a bubble is formed. The economic or financial bubble, especially its dynamics, is an intriguing topic that has been attracting longstanding attention. To illustrate the dynamics of the local explosion itself, the paper presents a novel, simple, yet useful time series model, called the stochastic nonlinear autoregressive model, which is always strictly stationary and geometrically ergodic and can…

推进投资组合优化：自适应最小方差组合与最小风险率框架AI 解读

This study presents the Adaptive Minimum-Variance Portfolio (AMVP) framework and the Adaptive Minimum-Risk Rate (AMRR) metric, innovative tools designed to optimize portfolios dynamically in volatile and nonstationary financial markets. Unlike traditional minimum-variance approaches, the AMVP framework incorporates real-time adaptability through advanced econometric models, including ARFIMA-FIGARCH processes and non-Gaussian innovations. Empirical applications on…

信息性缺失下的随机波动率建模AI 解读

Stochastic volatility models that treat the variance of a time series as a stochastic process have proven to be important tools for analyzing dynamic variability. Current methods for fitting and conducting inference on stochastic volatility models are limited by the assumption that any missing data are missing at random. With a recent explosion in technology to facilitate the collection of dynamic self-response data for which mechanisms underlying missing data are inherently…

高维矩阵时间序列双向因子模型的因子数确定：基于白噪声的方法AI 解读

In this paper, we study a new two-way factor model for high-dimensional matrix-variate time series. To estimate the number of factors in this two-way factor model, we decompose the series into two parts: one being a non-weakly correlated series and the other being a weakly correlated noise. By comparing the difference between two series, we can construct white-noise based signal statistics to determine the number of factors in row loading matrix (column loading matrix).…

基数正则化的 Hawkes–Granger 模型AI 解读

We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing out that most of the existing sparse causal learning algorithms for the Hawkes process suffer from a singularity in maximum likelihood estimation. As a result, their sparse solutions can appear only as numerical artifacts. In this paper, we propose a mathematically well-defined sparse causal…

货币数量论还是价格数量论？历史、理论与计量分析AI 解读

This research studies the relation between money and prices and its practical implications analyzing quarterly data from United States (1959-2022), Canada (1961-2022), United Kingdom (1986-2022), and Brazil (1996-2022). The historical, logical, and econometric consistency of the logical core of the two main theories of money is analyzed using objective bayesian and frequentist machine learning models, bayesian regularized artificial neural networks, and ensemble learning. It…

非平稳矩阵值时间序列的协整关系检测AI 解读

This paper proposes a Matrix Error Correction Model to identify cointegration relations in matrix-valued time series. We hereby allow separate cointegrating relations along the rows and columns of the matrix-valued time series and use information criteria to select the cointegration ranks. Through Monte Carlo simulations and a macroeconomic application, we demonstrate that our approach provides a reliable estimation of the number of cointegrating relationships.

用于宏观经济监测的自适应移动平均AI 解读

The use of moving averages is pervasive in macroeconomic monitoring, particularly for tracking noisy series such as inflation. The choice of the look-back window is crucial. Too long of a moving average is not timely enough when faced with rapidly evolving economic conditions. Too narrow averages are noisy, limiting signal extraction capabilities. As is well known, this is a bias-variance trade-off. However, it is a time-varying one: the optimal size of the look-back window…

时变参数模型的动态三重伽马收缩过程先验AI 解读

Many existing shrinkage approaches for time-varying parameter (TVP) models assume constant innovation variances across time points, inducing sparsity by shrinking these variances toward zero. However, this assumption falls short when states exhibit large jumps or structural changes, as often seen in empirical time series analysis. To address this, we propose the dynamic triple gamma prior -- a stochastic process that induces time-dependent shrinkage by modeling dependence…

含即时效应的 VARMA 过程：全局马尔可夫性、忠实性与工具变量AI 解读

Causal reasoning has gained great attention over the last half century as it allows (or at least intends) to answer questions which go above those within the capabilities of classical inferential statistics using just observational data. So far, causal research has been focused mostly on the i.i.d. setting. However, many are the situations where there exists a non-trivial dependence structure between sequential observations. Motivated by this fact, the main purpose of this…

高维稀疏因子模型AI 解读

We consider the estimation of a sparse factor model where the factor loading matrix is assumed sparse. The estimation problem is reformulated as a penalized M-estimation criterion, while the restrictions for identifying the factor loading matrix accommodate a wide range of sparsity patterns. We prove the sparsistency property of the penalized estimator when the number of parameters is diverging, that is the consistency of the estimator and the recovery of the true zeros…

在 Stata 中检验和估计时间序列与面板数据的结构突变AI 解读

Identifying structural change is a crucial step in analysis of time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed as a result of major disruptive events, such as the 2007--2008 financial crisis and the 2020 COVID--19 outbreak. Detecting the existence of breaks, and dating them is therefore necessary, not only for estimation purposes but also for understanding drivers of change and their effect on…

具有双向门限的矩阵自回归模型AI 解读

Recently, matrix-valued time series data have attracted significant attention in the literature with the recognition of threshold nonlinearity representing a significant advance. However, given the fact that a matrix is a two-array structure, it is unfortunate, perhaps even unusual, for the threshold literature to focus on using the same threshold variable for the rows and the columns. In fact, evidence in economic, financial, environmental and other data shows advantages of…

预测表现驱动因素的度量：信用评分应用AI 解读

As they play an increasingly important role in determining access to credit, credit scoring models are under growing scrutiny from banking supervisors and internal model validators. These authorities need to monitor the model performance and identify its key drivers. To facilitate this, we introduce the XPER methodology to decompose a performance metric (e.g., AUC, $R^2$) into specific contributions associated with the various features of a forecasting model. XPER is…

动态 CoVaR 建模与估计AI 解读

The popular systemic risk measure CoVaR (conditional Value-at-Risk) and its variants are widely used in economics and finance. In this article, we propose joint dynamic forecasting models for the Value-at-Risk (VaR) and CoVaR. The CoVaR version we consider is defined as a large quantile of one variable (e.g., losses in the financial system) conditional on some other variable (e.g., losses in a bank's shares) being in distress. We introduce a two-step M-estimator for the…

监控多元时间序列时第二阶段 Hotelling T² 控制图的表现AI 解读

Thanks to high-tech measurement systems like sensors, data are often collected with high frequency in modern industrial processes. This phenomenon could potentially produce autocorrelated and cross-correlated measurements. It has been shown that if this issue is not properly accounted for while designing the control charts, many false alarms may be observed, disrupting the monitoring process efficiency. There are generally two recommended ways to monitor autocorrelated data:…

用于水文推断的高斯滑动窗口回归模型AI 解读

Statistical models are an essential tool to model, forecast and understand the hydrological processes in watersheds. In particular, the understanding of time lags associated with the delay between rainfall occurrence and subsequent changes in streamflow is of high practical importance. Since water can take a variety of flow paths to generate streamflow, a series of distinct runoff pulses may combine to create the observed streamflow time series. Current state-of-the-art…