每日简报

Two frequent approaches for identifying structural VARs are external instruments, which carry economic content but are often weak, and non-Gaussianity of the shocks, which provides statistical identification but carries no economic meaning. We combine the two strategies in a single generalized method of moments framework. This hybrid approach point-identifies the target shocks while also identifying the non-target shocks up to sign and ordering. Under suitable rank…

简单、混合与多重季节自相关下的综合拟合优度检验AI 解读

Standard diagnostic procedures for assessing the goodness of fit of linear models include tests of the null hypothesis of no residual autocorrelation against the alternative of linear dependence. The literature proposes several portmanteau tests for residual autocorrelation in SARMA models, mainly focusing on two cases: short-term and single-seasonal autocorrelation. Since many time series exhibit multiple seasonal patterns, whose periodic components may interact with one…

高维 Ornstein–Uhlenbeck 过程漂移项的稀疏估计AI 解读

We study sparsity-regularized maximum likelihood estimation for the drift parameter of high-dimensional non-stationary Ornstein--Uhlenbeck processes given repeated measurements of i.i.d. paths. In particular, we show that Lasso and Slope estimators can achieve the minimax optimal rate of convergence. We exhibit numerical experiments for sparse estimation methods and show their performance.

标量对函数回归中的函数型预测变量选择与平滑系数估计AI 解读

In the framework of scalar-on-function regression models, in which several functional variables are employed to predict a scalar response, we propose a methodology for selecting relevant functional predictors while simultaneously providing accurate smooth (or, more generally, regular) estimates of the functional coefficients. We suppose that the functional predictors belong to a real separable Hilbert space, while the functional coefficients belong to a specific subspace of…

Comparing dependence structures between two related multivariate time series is of fundamental interest in many scientific applications, where changes may occur in both directed temporal interactions and contemporaneous connectivity. Modeling each time series by a vector autoregressive (VAR) model, we propose a new framework for estimation and inference of differential Granger causality (DiffGC) and differential network (DiffNet) structures in high dimensions. The proposed…

Wigner 矩阵时间序列的相变监测AI 解读

We develop methodology and theory for the detection of a phase transition in a time-series of high-dimensional random matrices. In the model we study, at each time point \( t = 1,2,\ldots \), we observe a deformed Wigner matrix \( \mathbf{M}_t \), where the unobservable deformation represents a latent signal. This signal is detectable only in the supercritical regime, and our objective is to detect the transition to this regime in real time, as new matrix--valued…

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…

We propose a dynamic multiplicative factor model for process data arising from complex problem-solving items, an emerging type of data in large-scale educational assessment. The proposed model can be viewed as an extension of the classical frailty models developed in survival analysis for multivariate recurrent event times, but with two important distinctions: (i) the factor (frailty) is of primary interest; (ii) covariates are internal and embedded in the factor. It allows…