论文检索

矩阵值函数平滑特征向量的√2估计AI 解读

Summary Modern statistical methods for multivariate time series rely on the eigendecomposition of matrix-valued functions such as time-varying covariance and spectral density matrices. The curse of indeterminacy or misidentification of smooth eigenvector functions has not received much attention. We resolve this important problem and recover smooth trajectories by examining the distance between the eigenvectors of the same matrix-valued function evaluated at two consecutive…

单回归Granger因果估计量的抽样分布AI 解读

Summary The single-regression Granger–Geweke causality estimator has previously been shown to solve known problems associated with the more conventional likelihood ratio estimator; however, its sampling distribution has remained unknown. We show that, under the null hypothesis of vanishing Granger causality, the single-regression estimator converges to a generalized χ2 distribution, which is well approximated by a Γ distribution. We show that this holds too for Geweke’s…

以子抽样扩展频域自助法的有效性AI 解读

Summary Bootstrapping spectral mean statistics has been a notoriously difficult problem over the past 25 years. Many frequency domain bootstraps are valid only for certain time series structures, e.g., linear processes, or for special types of statistics, i.e., ratio statistics, because such bootstraps fail to capture the limiting variance of spectral statistics in general settings. We address this issue with a different form of resampling, namely, subsampling. While not…

高维半参数高斯 Copula 向量自回归模型的估计与推断AI 解读

This paper develops simple, robust estimation and inference methods for the transition matrix of a high-dimensional semiparametric Gaussian copula vector autoregressive process. Our estimator is based on rank estimators of the large variance and auto-covariance matrices of a transformed latent high-dimensional Gaussian process. We derive rates of convergence of our estimator based on which we develop de-biased inference for Granger causality. Numerical results demonstrate…

广义指数加权移动平均分位数模型及其统计推断AI 解读

The exponentially weighting scheme is a simple and pragmatic approach to compute the value at risk (VaR). However, the existing exponentially weighting methods lack a sound statistical inference procedure. To circumvent this deficiency, this paper proposes a new generalized exponentially weighted moving average (GEWMA) quantile model, which allows a much broader weighting scheme than the benchmark one used in “Risk Metrics” document. For the GEWMA quantile model, a…

识别间歇性的收益可预测性AI 解读

Return predictability has been one of the central research questions in finance for many decades. This paper proposes a predictive regression with multiple structural changes to capture the sporadic predictive ability of potential predictors for the return series. An adaptive group Lasso procedure, augmented with a forward regression for break screening, is adopted to efficiently and consistently identify the structural breaks in the predictive regression, with predictors…

工具相关性时变下美国货币政策冲击的汇率效应与溢出识别AI 解读

Summary We propose a novel econometric approach to estimating time‐varying policy effects using external instruments in the presence of time‐varying instrument relevance in a factor‐augmented VAR model with data on the United States, Canada, Germany, Japan, and the United Kingdom. We find that US monetary policy shocks are an important driver of the exchange rate movements, with no delayed overshooting. We show that estimates of spillover effects of US monetary policy shocks…

向量自回归模型中 Kilian–Lewis 式反事实分析的直接方法AI 解读

Summary This paper proposes a direct approach to Kilian–Lewis style counterfactual analysis in structural vector autoregression models. The proposed approach is easy to implement, and the procedure of counterfactual construction is more transparent than the indirect approach. Practitioners conducting Kilian–Lewis style counterfactual analysis should find the proposed approach novel and useful.

低秩高维向量自回归模型的多重变点检测AI 解读

We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structure. We first address the problem of detecting a single change point using an exhaustive search algorithm and establish a finite sample error bound for its accuracy. Next, we extend the results to the case of multiple change points that can grow as a function of the sample size. Their detection is…

函数型数据序列周期图的最大值AI 解读

We study the periodogram operator of a sequence of functional data. Using recent advances in Gaussian approximation theory, we derive the asymptotic distribution of the maximum norm over all fundamental frequencies. We consider the case where the noise variables are independent and then generalize our results to functional linear processes. Our theory can be used for detecting periodic signals in functional time series when the length of the period is unknown. We demonstrate…

分而治之：大规模时间序列的分布式层级因子建模AI 解读

This paper proposes a hierarchical approximate-factor approach to analyzing high-dimensional, large-scale heterogeneous time series data using distributed computing. The new method employs a multiple-fold dimension reduction procedure using Principal Component Analysis (PCA) and shows great promises for modeling large-scale data that cannot be stored nor analyzed by a single machine. Each computer at the basic level performs a PCA to extract common factors among the time…

具有因子结构的大型面板的稳健估计AI 解读

This article studies estimation of linear panel regression models with heterogeneous coefficients using a class of weighted least squares estimators, when both the regressors and the error possibly contain a common latent factor structure. Our theory is robust to the specification of such a factor structure because it does not require any information on the number of factors or estimation of the factor structure itself. Moreover, our theory is efficient, in certain…

利用高峰度经济冲击识别结构向量自回归AI 解读

We revisit the generalized method of moments (GMM) estimation of the non-Gaussian structural vector autoregressive (SVAR) model. It is shown that in the n-dimensional SVAR model, global and local identification of the contemporaneous impact matrix is achieved with as few as n2+n(n−1)/2 suitably selected moment conditions, when at least n – 1 of the structural errors are all leptokurtic (or platykurtic). We also relax the potentially problematic assumption of mutually…

异方差随机系数自回归模型的变点检测AI 解读

We propose a family of CUSUM-based statistics to detect the presence of changepoints in the deterministic part of the autoregressive parameter in a Random Coefficient Autoregressive (RCA) sequence. Our tests can be applied irrespective of whether the sequence is stationary or not, and no prior knowledge of stationarity or lack thereof is required. Similarly, our tests can be applied even when the error term and the stochastic part of the autoregressive coefficient are non…

基于极端依赖的时间序列模型设定检验AI 解读

We propose a specification test for conditional location–scale models based on extremal dependence properties of the standardized residuals. We do so comparing the left-over serial extremal dependence—as measured by the pre-asymptotic tail copula—with that arising under serial independence at different lags. Our main theoretical results show that the proposed Portmanteau-type test statistics have nuisance parameter-free asymptotic limits. The test statistics are easy to…

Google 数据何时有助于 GDP 即时预测？预筛选与收缩方法AI 解读

Alternative datasets are widely used for macroeconomic nowcasting together with machine learning–based tools. The latter are often applied without a complete picture of their theoretical nowcasting properties. Against this background, this article proposes a theoretically grounded nowcasting methodology that allows researchers to incorporate alternative Google Search Data (GSD) among the predictors and that combines targeted preselection, Ridge regularization, and…

广义协方差估计量AI 解读

We consider a class of semi-parametric dynamic models with iid errors, including the nonlinear mixed causal-noncausal Vector Autoregressive (VAR), Double-Autoregressive (DAR) and stochastic volatility models. To estimate the parameters characterizing the (nonlinear) serial dependence, we introduce a generic Generalized Covariance (GCov) estimator, which minimizes a residual-based multivariate portmanteau statistic. In comparison to the standard methods of moments, the GCov…

事件时点观测的函数型数据稳定性检验及 IPO 表现应用AI 解读

Many sequentially observed functional data objects are available only at the times of certain events. For example, the trajectory of stock prices of companies after their initial public offering (IPO) can be observed when the offering occurs, and the resulting data may be affected by changing circumstances. It is of interest to investigate whether the mean behavior of such functions is stable over time, and if not, to estimate the times at which apparent changes occur. Since…

结合符号限制与外部工具变量识别 SVAR 模型AI 解读

We discuss combining sign restrictions with information in external instruments (proxy variables) to identify structural vector autoregressive (SVAR) models. In one setting, we assume the availability of valid external instruments. Sign restrictions may then be used to identify further orthogonal shocks, or as an additional piece of information to pin down the shocks identified by the external instruments more precisely. In a second setting, we assume that proxy variables…

关于后向平滑算法AI 解读

In the context of state-space models, skeleton-based smoothing algorithms rely on a backward sampling step, which by default, has a O(N2) complexity (where N is the number of particles). Existing improvements in the literature are unsatisfactory: a popular rejection sampling-based approach, as we shall show, might lead to badly behaved execution time; another rejection sampler with stopping lacks complexity analysis; yet another MCMC-inspired algorithm comes with no…

条件分位数覆盖率的样本外检验及增长风险应用AI 解读

This paper proposes tests for out-of-sample comparisons of interval forecasts based on parametric conditional quantile models. The tests rank the distance between actual and nominal conditional coverage with respect to the set of conditioning variables from all models, for a given loss function. We propose a pairwise test to compare two models for a single predictive interval. The set-up is then extended to a comparison across multiple models and/or intervals. The limiting…

马尔可夫转换 VAR 模型中的矩、冲击与溢出AI 解读

To investigate how economies, financial markets or institutions can deal with stress, we often analyze the effects of shocks conditional on being in a recession or a bear market. MSVAR models are perfectly suited for such analyses because they combine gradual movements with sudden regime switches. In this paper, we develop a comprehensive methodology to conduct these analyses. We derive first and second moments conditional only on the regime distribution and propose impulse…

北极海冰何时消失？面积、范围、厚度与体积预测AI 解读

Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change. We provide point, interval, and density forecasts for four measures of Arctic sea ice: area, extent, thickness, and volume. Importantly, we enforce the joint constraint that these measures must simultaneously arrive at an ice-free Arctic. We apply this constrained joint forecast procedure to models relating sea ice to atmospheric carbon dioxide concentration and models relating…

DSGE 模型全局识别问题的一种解法AI 解读

We develop an analytical framework to study global identification in structural models with forward-looking expectations. Our identification condition combines the similarity transformation linking the observationally equivalent state space systems with the constraints imposed on them by the model parameters. The key step of solving the identification problem then reduces to finding all roots of a system of polynomial equations. We show how it can be done using the concept…