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具有时变交互固定效应的时变面板数据模型AI 解读

This paper introduces a time-varying (TV) panel data model with interactive fixed effects where both the coefficients and factor loadings are allowed to change smoothly over time. We propose a local version of the least squares and principal component method to estimate the TV coefficients, TV factor loadings, and common factors simultaneously. We provide a bias-corrected local least squares estimator for the TV coefficients and establish the limiting distributions and…

高维时间序列时变网络的估计AI 解读

We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages. Under the sparse structural assumption, we propose a penalised local linear method…

非参数时变工具变量结构 VAR：估计与推断AI 解读

Abstract This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even…

网络时间序列中的欧氏镜像与动态变化AI 解读

Analyzing changes in network evolution is central to statistical network inference. We consider a dynamic network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection probabilities are functions of these vectors. Under mild assumptions, the evolution of latent vectors exhibits low-dimensional manifold structure under a suitable distance. This distance can be approximated by a measure of separation between the…

高维点过程网络的统计推断AI 解读

Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work has primarily addressed estimation. To bridge this gap, we develop a new statistical inference procedure for high-dimensional Hawkes…

异方差高维分位数回归的多重变点检测与定位AI 解读

Data heterogeneity is a challenging issue for modern statistical data analysis. There are different types of data heterogeneity in practice. In this article, we consider potential structural changes and complicated tail distributions. There are various existing methods proposed to handle either structural changes or heteroscedasticity. However, it is difficult to handle them simultaneously. To overcome this limitation, we consider statistically and computationally efficient…

空间变系数广义部分线性模型的分布式异质性学习AI 解读

Spatial heterogeneity is of great importance in social, economic, and environmental science studies. The spatially varying coefficient model is a popular and effective spatial regression technique to address spatial heterogeneity. However, accounting for heterogeneity comes at the cost of reducing model parsimony. To balance flexibility and parsimony, this article develops a class of generalized partially linear spatially varying coefficient models which allow the inclusion…

零利率下限下的平滑影子利率动态 Nelson–Siegel 模型AI 解读

We propose a smooth shadow-rate version of the dynamic Nelson-Siegel (DNS) model to analyze the term structure of interest rates during a zero lower bound (ZLB) period.By relaxing the no-arbitrage restriction, our shadow-rate model becomes highly tractable with a closed-form yield curve expression.This permits the implementation of readily available DNS extensions such as allowing for time-varying parameters and the integration of macroeconomic variables.Using U.S. Treasury…

适应趋势与方差变化的贝叶斯变点分析及局部异常值评分AI 解读

We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesian Changepoints with Outliers (ABCO). We use a state-space approach to identify a dynamic signal in the presence of outliers and measurement error with stochastic volatility. We find that global state equation parameters are inadequate for most real applications and we include local parameters to…

随机几何对象时间序列的形状分析统计框架AI 解读

We introduce a new framework to analyze shape descriptors that capture the geometric features of an ensemble of point clouds. At the core of our approach is the point of view that the data arises as sampled recordings from a metric space-valued stochastic process, possibly of nonstationary nature, thereby integrating geometric data analysis into the realm of functional time series analysis. Our framework allows for natural incorporation of spatial-temporal dynamics,…

具有异质性的非参数时变面板模型AI 解读

Since Bai (2009, Econometrica 77, 1229–1279), considerable extensions have been made to panel data models with interactive fixed effects (IFEs). However, little work has been conducted to understand the associated iterative algorithm, which, to the best of our knowledge, is the most commonly adopted approach in this line of research. In this paper, we refine the algorithm of panel data models with IFEs using the nuclear-norm penalization method and duple least-squares (DLS)…

汇率、无抛补利率平价与时变 Fama 回归AI 解读

ABSTRACT This paper studies the forward premium puzzle, which signals a violation of the uncovered interest parity (UIP) hypothesis. We test this hypothesis with Fama‐style regressions with time‐varying parameters (TVPs) and stochastic volatility (SV) on six major currencies relative to the US dollar on monthly samples from 1993 to 2018. TVP‐SV regressions are also employed to examine the opposing predictions of the forward premium and excess volatility puzzles often found…

利用联合特征函数对多元时间序列进行非参数分段AI 解读

Summary Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between the time series and its lagged values, our proposed method is able to detect changepoints in the marginal distribution, but also those in possibly nonlinear serial…

条件谱方法AI 解读

We model predictive scale-specific cycles. By employing suitable matrix representations, we express the forecast errors of covariance-stationary multivariate time series in terms of conditionally orthonormal scale-specific bases. The representations yield conditionally orthogonal decompositions of these forecast errors. They also provide decompositions of their variances and betas in terms of scale-specific variances and betas capturing predictive variability and…

期限结构模型中风险溢价的识别稳健推断AI 解读

We propose identification robust statistics for testing hypotheses on the risk premia in dynamic affine term structure models. We do so using the moment equation specification proposed in Adrian et al. (2013). Statistical inference based on their three-stage estimator requires knowledge of the risk factors’ quality and can be misleading when the β’s are weak, which results when sampling errors are of comparable order of magnitude as the risk factor loadings. We extend the…

美国货币政策与均衡不确定性AI 解读

ABSTRACT In this paper, I investigate the stance of the US monetary policy in the postwar period. To this end, I show that two features are key: a rich structural model and a parameterization of the indeterminate solution that ensures a sufficient exploration of the indeterminacy region during the estimation procedure. Therefore, I estimate a medium‐scale model on the US macroeconomic data using a novel solution method to allow for indeterminacy. The evidence of a passive…

菲利普斯曲线的断点：来自面板数据的证据AI 解读

ABSTRACT We revisit the Phillips curve, applying new Bayesian panel methods with structural breaks to US and EU disaggregate data. Our approach lets us estimate both the number and timing of breaks and to determine the existence of clusters of industries, cities, or countries whose Phillips curves display similar patterns. We find evidence of a flattening for US sectoral data and among EU countries, particularly poorer ones. Evidence of flattening is weaker for MSA‐level…

强化学习框架下的多变量动态中介分析AI 解读

Mediation analysis is an important analytic tool commonly used in a broad range of scientific applications. In this article, we study the problem of mediation analysis when there are multivariate and conditionally dependent mediators, and when the variables are observed over multiple time points. The problem is challenging, because the effect of a mediator involves not only the path from the treatment to this mediator itself at the current time point, but also all possible…

时变系数面板模型的设定检验AI 解读

This paper provides nonparametric specification tests for the commonly used homogeneous and stable coefficients structures in panel data models. We first obtain the augmented residuals by estimating the model under the null hypothesis and then run auxiliary time series regressions of augmented residuals on covariates with time-varying coefficients (TVCs) via sieve methods. The test statistic is then constructed by averaging the squared fitted values, which are close to zero…

信息性观测时点下的函数主成分分析AI 解读

Summary Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serve as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis, often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven…

纵向研究中序贯未测混杂下的敏感性模型与界限AI 解读

Abstract We consider sensitivity analysis for causal inference in a longitudinal study with time-varying treatments and covariates. It is of interest to assess the worst-case possible values of counterfactual outcome means and average treatment effects under sequential unmeasured confounding. We formulate several multi-period sensitivity models to relax the corresponding versions of the assumption of sequential non-confounding. The primary sensitivity model involves only…

通过贝叶斯线性规划控制信号发现与定位AI 解读

Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identify the exact locations of causal variants. So the statistical task is to output as many disjoint regions containing a signal as possible, each as small as possible, while controlling false positives. Similar problems arise in any application where signals cannot be perfectly localized, such as…

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具有趋势性时变系数的空间面板数据模型AI 解读

This article investigates the estimation and inference of spatial panel data models in which the regression coefficient vector is a trending function. We use time differences to eliminate the individual effects and employ various GMM estimations for regression coefficients with both linear and quadratic moments. Time trend estimator based on these GMM estimations is also proposed. Monte Carlo experiments show that the finite sample performance of the estimators is…

结构不稳定下的面板数据协整检验AI 解读

Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article.The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components.We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran's common correlated effects approach.This…