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多元时间序列频域中的局部化稀疏主成分分析AI 解读

Principal component analysis has been a main tool in multivariate analysis for estimating a low dimensional linear subspace that explains most of the variability in the data. However, in high-dimensional regimes, naive estimates of the principal loadings are not consistent and difficult to interpret. In the context of time series, principal component analysis of spectral density matrices can provide valuable, parsimonious information about the behavior of the underlying…

整数值平滑转换 GARCH 模型：设定与检验AI 解读

This paper introduces a new class of nonlinear models known as the Z-valued smooth transition GARCH model, designed to accommodate Z-valued time series that display asymmetric, nonlinear and highly persistent volatility. The paper outlines the maximum likelihood estimation procedure and establishes its consistency and asymptotic normality of the estimated parameters. Three types of tests are studied, including sup-type linearity test, score-based goodness-of-fit test, and…

主特征值与主特征向量的检验AI 解读

We establish central limit theorems for principal eigenvalues and eigenvectors under divergent spiked covariance models, and develop three two-sample tests for testing (1) the equality of principal eigenvalues, (2) the stability of eigenvalue proportions, and (3) the invariance of principal eigenvectors. One important application of our results is to understand structural breaks in large factor models. That is, when a structural break is suspected or detected, these tests…

流形时间序列的平稳性AI 解读

In modern interdisciplinary research, manifold time series data have been garnering more attention. A critical question in analyzing such data is “stationarity”, which reflects the underlying dynamic behavior and is crucial across various fields like cell biology, neuroscience and empirical finance. Yet, there has been an absence of a formal definition of stationarity that is tailored to manifold time series. This work bridges this gap by proposing the first definitions of…

非零中位数 GARCH 型噪声下重尾 AR 模型的两步估计AI 解读

This paper develops a novel two-step estimating procedure for heavy-tailed AR models with nonzero median GARCH-type noises, allowing for time-varying volatility. We first establish the self-weighted quantile regression estimator (SQE) across all quantile levels τ∈(0,1) for the AR parameters θ0. We show that the SQE, less a bias, converges weakly to a Gaussian process at a rate of n−1/2. The bias is zero if and only if τ equals τ0, the probability that the noise is less than…

矩阵时间序列 CP 因子模型的识别与估计AI 解读

We propose a new method for identifying and estimating the CP-factor models for matrix time series. Unlike the generalized eigenanalysis-based method (J. R. Stat. Soc. Ser. B. Stat. Methodol. 85 (2023) 127–148) for which the convergence rates of the associated estimators may suffer from small eigengaps as the asymptotic theory is based on some matrix perturbation analysis, the proposed new method enjoys faster convergence rates which are free from any eigengaps. It achieves…

非平稳厚尾异方差噪声下中位数自回归模型的推断AI 解读

This article studies estimation and inference in the autoregressive (AR) models with unspecified and heavy-tailed heteroskedastic noises. A piece-wise locally stationary structure of the noise is constructed to capture various forms of heterogeneity, without imposing any restrictions on the tail index. The new nonstationary AR model allows for not only time-varying conditional features but also unconditional variance and tail index. This makes it appealing in practice, with…

数据丰富环境中的时变完全子集平均AI 解读

This study proposes two novel time-varying model-averaging methods for time-varying parameter regression models. When the number of predictors is small, we propose a novel time-varying complete subset-averaging (TVCSA) procedure, where the optimal time-varying subset size is obtained by minimizing the local leave- h -out cross-validation criterion. The TVCSA method is asymptotically optimal for achieving the lowest possible local mean squared error. When the number of…

随机系数自回归模型的实时变点监测AI 解读

We propose a family of weighted statistics based on the CUSUM process of the WLS residuals for the online detection of changepoints in a Random Coefficient Autoregressive model, using both the standard CUSUM and the Page-CUSUM process. We derive the asymptotics under the null of no changepoint for all possible weighing schemes, including the case of the standardized CUSUM, for which we derive a Darling–Erdös-type limit theorem; our results guarantee the procedure-wise size…

含潜变量的马尔可夫过程学习与识别AI 解读

We consider the problem of identifying the parameters of a time-homogeneous bivariate Markov chain when only one of the two variables is observable. We show that, subject to conditions that we spell out, the transition kernel and the distribution of the initial condition are uniquely recoverable (up to an arbitrary relabelling of the state space of the latent variable) from the joint distribution of four (or more) consecutive time-series observations. The result is,…

相关时间序列的预测AI 解读

ABSTRACT A collection of time series are “related” if they follow similar stochastic processes and/or they are statistically dependent. This paper proposes a related time series (RTS) forecasting model that exploits these relationships. The model's foundation is a set of univariate Gaussian autoregressions, one for each series, which are then augmented to incorporate stochastic volatility, heavy‐tailed innovations, additive outliers, time‐varying parameters and common…

分布向量自回归：刻画宏观与金融依赖AI 解读

ABSTRACT This paper extends the vector autoregression framework by introducing a flexible distributional regression that models multivariate time series without imposing restrictive parametric distribution assumptions. We develop a distributional impulse response function that captures the future effect of distributional disturbances within the system, providing a more detailed view of dynamic heterogeneity. We propose a straightforward estimation method and establish its…

利用文本数据的因子分位数预测AI 解读

ABSTRACT Words matter for predicting tail risks. We propose an attention mechanism embedded in a quantile factor model, yielding information that is quantile‐specific, target‐specific, and horizon‐specific. We establish new asymptotic results and show empirically that targeted textual data improve quantile forecasts of exchange‐rate returns and industrial production growth relative to strong benchmarks and other conditional‐quantile models. Bigrams and trigrams drive these…

什么解释了国际利率的共同变动？AI 解读

ABSTRACT The international co‐movement of interest rates reflects correlated business‐cycle fluctuations, largely driven by demand shocks. Monetary policy in advanced economies follows domestic mandates—inflation and the output gap—and does not respond to foreign policy shocks. We derive this result from a Bayesian structural panel vector autoregression with informative priors, homogeneity restrictions on contemporaneous relations, a hierarchical Minnesota prior with…

时间依赖球面随机场的多尺度 CUSUM 检验AI 解读

This paper investigates the asymptotic behavior of structural break tests in the harmonic domain for time dependent spherical random fields. In particular, we prove a functional central limit theorem result for the fluctuations over time of the sample spherical harmonic coefficients, under the null of isotropy and stationarity; furthermore, we prove consistency of the corresponding CUSUM test, under a broad range of alternatives, including deterministic trend, abrupt change,…

含局部测量误差的非平稳函数型时间序列结构变化推断AI 解读

Abstract We study the problem of detecting and localizing change points for a general class of locally stationary functional time series. To accommodate the nonstationarity and other possible complex features, such as discontinuous trajectories, and heterogeneous partial measurement error of contemporary functional data, we propose methods that do not rest on the preprocessing techniques of presmoothing and dimension-reduction, which would be less accurate without the…

非高斯结构向量自回归中的时变冲击传导AI 解读

Summary This paper analyses possibly time-varying shock transmission in structural vector autoregressive (VAR) models when the reduced-form VAR coefficients are time invariant and the shocks are identified through non-Gaussianity. To check for possible time variation in the impulse responses, we propose Wald tests for two situations: (1) homoskedastic and (2) heteroskedastic structural shocks with changes in the unconditional variances. For the latter case, the challenge is…

结合统计识别与潜在内生代理变量估计财政乘数AI 解读

Summary Different proxy variables used in fiscal policy structural vector autoregressions (SVARs) lead to contradicting conclusions regarding the size of fiscal multipliers. Our analysis suggests that the conflicting results may stem from violations of the proxy exogeneity assumptions. We propose a novel approach to include proxy variables in a Bayesian non-Gaussian SVAR, tailored to accommodate potentially endogenous proxies. Using our model, we find that increasing…

混合矩阵值自回归模型AI 解读

Time series of matrix-valued data are increasingly available in various areas including economics, finance, social science, among others. These data may shed light on the inter-dynamical relationships between two sets of attributes, for instance, countries and economic indices. The matrix autoregressive (MAR) model provides a parsimonious approach for analyzing such data. However, the MAR model, being a linear model with parametric constraints, cannot capture the nonlinear…

得分驱动模型时变参数的一致性、分布收敛与最优性AI 解读

Observation-driven models for time series have a long history in statistics and econometrics, but are typically studied under the assumption of correct dynamic specification. We develop an in-fill asymptotic framework to study the limiting behavior of the estimated (i.e., filtered) time-varying parameter paths obtained with such models in (severely) mis-specified settings. We show that despite such mis-specification, the filtered paths, particularly those from the class of…

高维预测回归中的 LASSO 推断AI 解读

LASSO inflicts shrinkage bias on estimated coefficients, which undermines asymptotic normality and invalidates standard inferential procedures based on the t -statistic. Given cross sectional data, the desparsified LASSO has emerged as a well-known remedy for correcting the shrinkage bias. In the context of high dimensional predictive regression, the desparsified LASSO faces an additional challenge: the Stambaugh bias arising from nonstationary regressors modeled as local…

分数阶单整模型的修正条件平方和估计量AI 解读

In this paper, we analyse the influence of estimating a constant term on the bias of the conditional sum-of-squares (CSS) estimator in a stationary or non-stationary type-II ARFIMA (p1, d, p2) model. We derive expressions for the estimator’s bias and show that the leading term can be easily removed by a simple modification of the CSS objective function. We call this new estimator the modified conditional sum-of-squares (MCSS) estimator. We show theoretically and by means of…

集合识别 SVAR 预测误差方差分解的估计与推断AI 解读

We study estimation and inference of the Forecast Error Variance Decomposition (FEVD) in Structural Vector Autoregressions (SVARs) that are set-identified by internal and external (proxy) restrictions. The FEVD measures the importance of shocks for macroeconomic fluctuations and is thus key to business cycle analysis. We deliver three practitioner-oriented results. First, we characterize the endpoints of the FEVD as the extreme eigenvalues of a symmetric reduced-form matrix,…

联合尾部风险监测：增长与通胀的应用AI 解读

This paper develops the concept of Growth and Inflation at Risk frontier (GIaR). This is a bivariate generalisation of the concepts of Growth-at-Risk (GaR) and Inflation-at-Risk (IaR). We propose a novel approach to identify and estimate GIaR and provide uniformly valid upper and lower confidence bands. We first apply our procedure to predict the conditional probability of stagflation. Second, we compute worst-case scenarios for a policy maker who is concerned about the…