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基于奇异值分解与小波阈值的混频时间序列季节调整AI 解读

In this article, we propose a novel seasonal adjustment method that accommodates time series observed at mixed frequencies and possessing possibly multiple abrupt changes in seasonality. We assume that the observed series is a known linear transformation of an underlying high frequency series whose seasonal component can be represented as a matrix with a low rank Singular Value Decomposition (SVD) structure, and the nonseasonal component is difference stationary. The right…

响应缺失空间面板模型的固定效应估计：美国州税收竞争应用AI 解读

We consider estimation and inferences for general spatial panel data models with randomly missing observations on responses. It allows for unobserved spatiotemporal heterogeneity, time-varying endogenous and contextual spatial interactions, time-varying cross-sectional error dependence, and serial correlation. A general M-estimation method is proposed for model estimation and a novel corrected plug-in method is proposed for model inference. Both take into account the…

小簇样本下聚类数据分析的同质性寻求AI 解读

Clustered data analysis is an important topic in data science. A well established approach is to assume all clusters share the same unknown parameters of interest, and the difference between different clusters is formulated and accounted for by cluster effects. Whilst this approach works very well in many issues, such as exploring the global impact of an explanatory variable on the response variable, it does not provide much insight about individual attributes of each…

GARCH-MIDAS 模型的自适应组 LASSOAI 解读

This article investigates the variable selection problem in the generalized autoregressive conditional heteroscedasticity-mixed-data sampling (GARCH-MIDAS) model of Engle, Ghysels, and Sohn (Citation2013), and establishes the corresponding theoretical properties along with a feasible computational algorithm. The proposed method is based on a penalized log-partial likelihood with an adaptively group-weighted L2 penalty on the long-term component coefficients. The variable…

高维空间面板网络的统一推断AI 解读

We propose employing a high-dimensional generalized method of moments (GMM) estimator, regularized for dimension reduction and subsequently debiased to correct for shrinkage bias (referred to as a debiased-regularized estimator), for inference on large-scale spatial panel networks. In particular, the network structure, which incorporates a flexible sparse deviation that can be regarded either as a latent component or as a misspecification of a predetermined adjacency matrix,…

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…

纯跳过程中的漂移爆发：检测及比特币应用AI 解读

This article proposes a new nonparametric test for detecting short-lived locally explosive trends (drift bursts) in pure-jump processes. The new test is designed specifically to detect intraday flash crashes and gradual jumps in cryptocurrency prices recorded at a high frequency. Empirical analysis shows that drift bursts in bitcoin price occur, on average, every second day. Their economic importance is highlighted by showing that hedge funds holding cryptocurrency in their…

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…

全职与兼职就业选择的重要性：估计工资曲线的新面板样本选择模型AI 解读

The literature has shown that correcting for self-selection into work is important for the estimation of wage profiles. In this paper, we analyze to what extent intensive labor supply choices add valuable otherwise unobserved information to improve wage profile estimates. We develop a panel data sample selection model that allows for discrete choices in labor supply decisions and apply this to high-quality administrative data. Compared to labor supply decisions at the…

利用终止行动识别具有双曲贴现的动态离散选择模型AI 解读

We study the identification of dynamic discrete choice models with hyperbolic discounting using a terminating action. We provide novel identification results for both sophisticated and naive agents’ discount factors and their utilities in a finite horizon framework under the assumption of a stationary flow utility. In contrast to existing identification strategies we do not require to observe the final period for the sophisticated agent. Moreover, we avoid normalizing 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…

有向网络半参数形成模型的推断AI 解读

We propose a semiparametric model for dyadic link formations in directed networks. The model contains a set of degree parameters that measure different effects of popularity or outgoingness across nodes, a regression parameter vector that reflects the homophily effect resulting from the nodal attributes or pairwise covariates associated with edges, and a set of latent random noises with unknown distributions. Our interest lies in inferring the unknown degree parameters and…

通过再生核希尔伯特空间中的函数迁移学习调整选择偏差AI 解读

As accessibility to different data sources increases, transfer learning becomes prevalent in improving statistical efficiency by integrating different datasets. In this article, we focus on the popular non-probability sampling from a finite population and propose a functional transfer learning method to adjust its selection bias through calibrating marginal means of functions in a reproducing kernel Hilbert space by incorporating a reference probability sample. Compared with…

动态分位数的函数系数 VAR 模型及非参数金融网络构建AI 解读

In this article, we investigate a functional coefficient vector autoregressive model for conditional quantiles, in which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system based on the deep learning method of neural networks and the local linear smoothing technique. We establish…

时间聚合对 MIDAS 回归的影响AI 解读

Time series used in practice are often temporal aggregates. A MIDAS regression model may be fitted wrongly assuming the independent variables are not aggregated when in fact they are. We derive the correct model’s specification under temporal aggregation of the independent variables, thus identifying the correct number of dynamic terms and the order of the MA component. We also show that these depend on the frequency of the variables and the aggregation order used. As a…

估计美式期权隐含的状态价格密度AI 解读

We propose a new method to estimate state price densities implicit in American-style options. The method involves estimating the parameters of a Gauss-Hermite series expansion and solving a sequence of recursive equations for the early exercise premium. The resulting estimator can capture sudden shifts in density that may occur during financial crises or in response to significant policy events. It also provides an estimate of the early exercise premium that is of…

高维函数时间序列的因子建模AI 解读

Many economic and scientific problems involve the analysis of high-dimensional functional time series, where the number of functional variables p diverges as the number of serially dependent observations n increases. In this paper, we present a novel functional factor model for high-dimensional functional time series that maintains and makes use of the functional and dynamic structure to achieve great dimension reduction and find the latent factor structure. To estimate the…