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删失生存数据中区分能力指标的高效非参数估计AI 解读

Abstract Discrimination measures such as the concordance index and the cumulative-dynamic time-dependent area under the ROC-curve are widely used in the medical literature for evaluating the predictive accuracy of a scoring rule which relates a set of prognostic markers to the risk of experiencing a particular event. Often the scoring rule being evaluated in terms of discriminatory ability is the linear predictor of a survival regression model such as the Cox proportional…

选民偏好的共同变动：超越民调的美国总统选举预测市场信息AI 解读

We propose a novel time-series econometric framework to forecast U.S. Presidential election outcomes in real time by combining polling data, economic fundamentals, and political prediction market prices. Our model estimates the joint dynamics of voter preferences across states. Applying our approach to the 2024 Presidential Election, we find a two-factor structure driving the vast majority of the variation in voter preferences. We identify electorally similar state clusters…

高频金融数据的大型瞬时波动率矩阵非参数估计AI 解读

In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalized shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot…

变系数 ARMA 模型的统一理论与显式解AI 解读

A new explicit solution representation is provided for ARMA recursions with drift and either deterministically or stochastically varying coefficients. It is expressed in terms of the determinants of banded Hessenberg matrices and, as such, is an explicit function of the coefficients. In addition to computational efficiency, the proposed solution provides a more explicit analysis of the fundamental properties of such processes, including their Wold–Cramér decomposition, their…

风险价值与期望损失联合回归的最优模型平均AI 解读

Since the implementation of the Basel III Accord, expected shortfall (ES) has gained increasing attention from regulators as a complement to value-at-risk (VaR). The problem of elicitability for ES makes jointly modeling VaR and ES a popular method to study ES. In this article, we develop model averaging for joint VaR and ES regression models that selects the two weight vectors by minimizing a jackknife criterion. We show the large sample properties of the estimators under…

非平稳时间序列非线性泛函的一般极限理论AI 解读

New limit theory is provided for a wide class of sample variance and covariance functionals involving both nonstationary and stationary time series. Sample functionals of this type commonly appear in regression applications and the asymptotics are particularly relevant to estimation and inference in nonlinear nonstationary regressions that involve unit root, local unit root, or fractional processes. The limit theory is unusually general in that it covers both parametric and…

带外生性预检验的识别稳健两阶段bootstrap检验AI 解读

Pretesting for exogeneity has become routine in many empirical applications involving instrumental variables (IVs) to decide whether the ordinary least squares or IV-based method is appropriate. Guggenberger (2010a, Econometric Theory , 26, 369–382) shows that the second-stage test – based on the outcome of a Durbin-Wu-Hausman-type pretest in the first stage – exhibits extreme size distortion, with asymptotic size equal to 1 when the standard critical values are used. In…

短暂不稳定情形下的预测能力比较AI 解读

Summary We consider forecast comparison in the presence of instability when this affects only a short period of time. We demonstrate that global tests do not perform well in this case because they were not designed to capture very short-lived instabilities, and their power vanishes altogether when the magnitude of the shock is very large. We then discuss non-parametric approaches that are more suitable to detect such situations. We illustrate these results in a Monte Carlo…

局部投影的统计推断AI 解读

Summary Inference for impulse responses estimated with local projections presents interesting challenges and opportunities. Analysts typically want to assess the precision of individual estimates, explore the dynamic evolution of the response over particular regions, and generally determine whether the impulse generates a response that is any different from the null of no effect. Each of these goals requires a different approach to inference. In this article, we provide an…

极端洪水保险损失的因子 Copula 潜在藤时间序列模型AI 解读

Statistical inference on the dependence of multivariate extremes poses notable challenges, particularly in contexts characterized by large dimensions and sparse extreme observations. While copula models provide flexible parametric methods for dependence modeling, caution is warranted when using them for extremal dependence inference or tail extrapolation. In this article, a novel class of factor-vine copula models is introduced. It is designed for modeling the dependence of…

高维一般学习程序的拟合优度评估AI 解读

Black-box learners have demonstrated remarkable success across various fields due to their high predictive accuracy. However, the complexity of their learning procedures poses significant challenges in evaluating whether a given learner has achieved optimal performance on datasets with unknown data-generating mechanisms. We propose a general goodness-of-fit test for assessing different learning procedures involving high-dimensional predictors, encompassing methods from…

SOFARI：基于流形的高维统计推断AI 解读

Multi-task learning is a widely used technique for harnessing information from various tasks. Recently, the sparse orthogonal factor regression (SOFAR) framework, based on the sparse singular value decomposition (SVD) within the coefficient matrix, was introduced for interpretable multi-task learning, enabling the discovery of meaningful latent feature-response association networks across different layers. However, conducting precise inference on the latent factor matrices…

高维向量自回归的数据驱动调参AI 解读

Lasso-type estimators are routinely used to estimate high-dimensional time series models. The theoretical guarantees established for these estimators typically require the penalty level to be chosen in a suitable fashion often depending on unknown population quantities. Furthermore, the resulting estimates and the number of variables retained in the model depend crucially on the chosen penalty level. However, there is currently no theoretically founded guidance for this…

谱密度函数的实用区间估计方法AI 解读

The spectral density function can play a key role in time series analysis, where nonparametric interval estimation of the spectral density is a fundamental issue. However, the prevailing pointwise interval methods for spectral densities, including chi-square approximation and frequency domain bootstrap (FDB), can be misleading in practice, perhaps more so than appreciated, as confidence intervals often exhibit low coverage accuracy as well as high sensitivity to tuning…

日内瞬时波动率向量的矩阵预测方法AI 解读

In this article, we introduce a novel method for predicting intraday instantaneous volatility based on Itô semimartingale models using high-frequency financial data. Several studies have highlighted stylized volatility time series features, such as interday auto-regressive dynamics and the intraday U-shaped pattern. To accommodate these volatility features, we propose an interday-by-intraday instantaneous volatility matrix process that can be decomposed into low-rank…

大型 VAR 中溢出发送组与接收组的识别AI 解读

I propose an algorithm that partitions the series of a large vector autoregression (VAR) into groups based on the spillover structure. The novelty of the procedure is that it is capable of simultaneously detecting both the giver and receiver group structures. I study the properties of the algorithm when the data are generated by a class of network-based VAR models and show that it consistently detects the groups within this class. The methodology is applied to study the…

经验学习下非平稳非线性队列面板的最小二乘估计AI 解读

We discuss techniques of estimation and inference for nonstationary nonlinear cohort panels with learning from experience, showing, inter alia, the consistency and asymptotic normality of the nonlinear least squares estimator used in empirical practice. Potential pitfalls for hypothesis testing are identified and solutions proposed. Monte Carlo simulations verify the properties of the estimator and corresponding test statistics in finite samples, while an application to a…

高维环境下的局部可预测性AI 解读

We propose a time series forecasting method designed to effectively handle large sets of predictive signals, many of which may be irrelevant or short-lived over time. Our method transforms predictive signals into candidate density forecasts via time-varying coefficient models, and subsequently combines them into an aggregate density forecast via time-varying subset combination. The approach is computationally efficient because it uses online prediction and updating. Through…

不利情景下的回归AI 解读

We introduce a new regression method that relates the mean of an outcome variable to covariates, under the “adverse condition” that a distress variable falls in its tail. This allows to tailor classical mean regressions to adverse scenarios, which receive increasing interest in economics and finance, among many others. In the terminology of the systemic risk literature, our method can be interpreted as a regression for the Marginal Expected Shortfall. We propose a two-step…

部分信息汇集的动态条件相关模型AI 解读

We propose a novel Dynamic Conditional Correlation model with Conditional Linear Information Pooling (CLIP-DCC) which endogenously determines an optimal degree of commonality in the correlation innovations. Effectively, this allows a part of the update of each individual correlation to parsimoniously depend on the information contained in all asset return pairs. In contrast to existing approaches, such as the Dynamic EquiCOrrelation (DECO) model, the CLIP-DCC model does not…

同质结构下的面板分位数 GARCH 模型AI 解读

Empirical evidence indicates that the estimates of GARCH parameters cluster in a panel of financial assets, potentially due to assets with similar exposure to common market risks. To capture the subgroup effect on conditional quantiles of financial asset returns and improve estimation efficiency by pooling information across individuals within the same group, this article introduces the panel quantile GARCH model with homogeneous structures in the coefficient functions. A…

对象值时间序列的变点检测AI 解读

This article is concerned with change point detection for object-valued data that reside in a metric space, which has attracted some recent interests in statistics and econometrics literature. The existing methods either focus on independent data or can only detect change in the Fréchet mean or variance. In this article, we propose a self-normalization (SN, hereafter) based statistic for detecting a shift in the marginal distribution of object-valued time series. Our test is…

记忆未知的强持久性数据的稳健趋势估计AI 解读

Economic analysis is often based on pre-filtered, de-trended, or seasonally adjusted data. Underlying filtering methods make strong assumptions about the memory of the series to be filtered, and inference about the memory is limited particularly when persistent cyclical variation overshadows the trend. This paper introduces a data-driven method for filtering persistent series that requires no prior assumptions about the memory, thus is robust to the actual memory of the…

聚类 GARCHAI 解读

We introduce a novel multivariate GARCH model with flexible convolution-t distributions that is applicable in high-dimensional systems. The model is called Cluster GARCH because it can accommodate cluster structures in the conditional correlation matrix and in tail dependencies. The expressions for the log-likelihood function and its derivatives are tractable, and the latter facilitate a score-driven model for the dynamic correlation structure. We apply the Cluster GARCH…