论文检索

多重变点及类似变点特征的最窄超阈值检测AI 解读

Summary We propose a new, generic and flexible methodology for non-parametric function estimation, in which we first estimate the number and locations of any features that may be present in the function and then estimate the function parametrically between each pair of neighbouring detected features. Examples of features handled by our methodology include change points in the piecewise constant signal model, kinks in the piecewise linear signal model and other similar…

允许内生性的回归结构变化无模型一致检验AI 解读

Structural instability leads to misleading inference and imprecise prediction of time series models that assume stationarity. We propose a model-free consistent test for structural change in regression by testing the instability of the Fourier transform of data. This novel approach avoids smoothed nonparametric estimation of the unknown regression function and so is free of the “curse of dimensionality” problem. Unlike the existing literature, we allow for endogenous and…

固定效应模型中的因变量缺失AI 解读

This paper considers estimation of linear fixed-effects models in which the dependent variable may be missing. For cross-sectional units with dependent variables missing, use of covariate information from all time periods can provide efficiency gains relative to complete-data methods. A classical minimum distance (CMD) estimator based upon Chamberlain (1982, 1984), which is consistent under a missing-at-random (MAR) type assumption, is proposed for the static fixed-effects…

高频数据中市场微观结构噪声的Hausman检验AI 解读

We develop tests that help assess whether a high frequency data sample can be treated as reasonably free of market microstructure noise at a given sampling frequency for the purpose of implementing high frequency volatility and other estimators. The tests are based on the Hausman principle of comparing two estimators, one that is efficient but not robust to the deviation being tested, and one that is robust but not as efficient. We investigate the asymptotic properties of…

通过平滑局部投影估计脉冲响应AI 解读

Local projections (LP) is a popular methodology for the estimation of impulse responses (IR). Compared to the traditional VAR approach, LP allow for more flexible IR estimation by imposing weaker assumptions on the dynamics of the data. The nonparametric nature of LP comes at an efficiency cost, and in practice, the LP estimator may suffer from excessive variability. In this work, we propose an IR estimation methodology based on B-spline smoothing called smooth local…

降采样与混频时间序列中结构向量自回归模型的可识别性与估计AI 解读

Summary Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale, we take a model-based approach that relies on structural vector…

去偏 Whittle 似然AI 解读

The Whittle likelihood is a widely used and computationally efficient pseudolikelihood. However, it is known to produce biased parameter estimates with finite sample sizes for large classes of models. We propose a method for debiasing Whittle estimates for second-order stationary stochastic processes. The debiased Whittle likelihood can be computed in the same |${O}(n\log n)$| operations as the standard Whittle approach. We demonstrate the superior performance of our method…

周期性到达率的超分辨率估计AI 解读

Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a nonhomogeneous Poisson process. The estimator is the super-resolution analogue to Shao (2010) and Shao and Lii [J. R. Stat. Soc. Ser. B. Stat. Methodol. 73 (2011) 99–122], which is a sum of $p$ sinusoids where $p$ and the amplitude and phase of each wave are not known and need to be estimated. This results in an interpretable yet flexible…

Wiener 泛函最大值的高斯近似及高频数据应用AI 解读

This paper establishes an upper bound for the Kolmogorov distance between the maximum of a high-dimensional vector of smooth Wiener functionals and the maximum of a Gaussian random vector. As a special case, we show that the maximum of multiple Wiener–Itô integrals with common orders is well approximated by its Gaussian analog in terms of the Kolmogorov distance if their covariance matrices are close to each other and the maximum of the fourth cumulants of the multiple…

基于最近邻的序贯变点检测AI 解读

We propose a new framework for the detection of change-points in online, sequential data analysis. The approach utilizes nearest neighbor information and can be applied to sequences of multivariate observations or non-Euclidean data objects, such as network data. Different stopping rules are explored, and one specific rule is recommended due to its desirable properties. An accurate analytic approximation of the average run length is derived for the recommended rule, making…

时间依赖观测下高维协方差与精度矩阵的估计AI 解读

We consider the estimation of large covariance and precision matrices from high-dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal dependence. The temporal dependence is allowed to be long-range so with longer memory than those considered in the current literature. We show that several commonly used methods for independent observations can be applied to the temporally dependent data. In particular, the rates of convergence are obtained for…

高维多响应张量回归的凸正则化AI 解读

In this paper, we present a general convex optimization approach for solving high-dimensional multiple response tensor regression problems under low-dimensional structural assumptions. We consider using convex and weakly decomposable regularizers assuming that the underlying tensor lies in an unknown low-dimensional subspace. Within our framework, we derive general risk bounds of the resulting estimate under fairly general dependence structure among covariates. Our framework…

一阶退化下的泛函推断AI 解读

This paper presents a unified framework for inference on parameters of the form ϕ(θ0), where θ0 is unknown but can be estimated by θˆn, and ϕ is known with null first order derivative at θ0. We show the “standard” bootstrap is consistent if and only if the second order derivative ϕθ0′′=0 under regularity conditions, thereby identifying a source of bootstrap failures distinct from that in Fang and Santos (2018). Two consistent bootstrap schemes are proposed: one based on Babu…

两两稳定网络矩的弱大数定律AI 解读

This paper studies the dependence structure of sparse networks realized according to a strategic model of network formation with homophilous agents. We argue that equilibrium selection and chains of indirect dependence generated by strategic interactions are the main drivers of network dependence. Drawing on results in random-graph theory, we derive weak-dependence conditions under which a law of large numbers holds for a general class of network moments. A key condition…

利用离散样本估计连续时间随机过程的长期方差AI 解读

This paper develops the methodology and asymptotic theory for the estimation of longrun variance of continuous time process. We analyze the asymptotic bias and variance of the longrun variance estimator in continuous time, and provide the optimal bandwidth balancing them off and minimizing the asymptotic mean squared error. In the paper, we present not only how to consistently estimate the longrun variance of continuous time process, but also how to choose bandwidth…

时间序列线性回归中 M 估计量的有界性AI 解读

We show boundedness in probability uniformly in sample size of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semicontinuous and sufficiently large for large argument. Particular cases are the Huber-skip and quantile regression. Boundedness requires an assumption on the frequency of small regressors. We show that this is satisfied for a variety of deterministic and stochastic…

频域中的弱平稳性检验AI 解读

We examine a test for weak stationarity against alternatives that covers both local-stationarity and break point models. A key feature of the test is that its asymptotic distribution is a functional of the standard Brownian bridge sheet in [0,1] 2 , so that it does not depend on any unknown quantity. The test has nontrivial power against local alternatives converging to the null hypothesis at a T −1/2 rate, where T is the sample size. We also examine an easy-to-implement…

缺失数据下时间序列回归的异方差与自相关稳健推断AI 解读

In this article, we investigate the properties of heteroskedasticity and autocorrelation robust (HAR) test statistics in time series regression settings when observations are missing. We primarily focus on the nonrandom missing process case where we treat the missing locations to be fixed as T → ∞ by mapping the missing and observed cutoff dates into points on [0,1] based on the proportion of time periods in the sample that occur up to those cutoff dates. We consider two…

多元正态性的刻画与拟合优度检验：含 GARCH 应用AI 解读

We provide novel characterizations of multivariate normality that incorporate both the characteristic function and the moment generating function, and we employ these results to construct a class of affine invariant, consistent and easy-to-use goodness-of-fit tests for normality. The test statistics are suitably weighted L 2 -statistics, and we provide their asymptotic behavior both for i.i.d. observations as well as in the context of testing that the innovation distribution…

向量时间序列共线性检验AI 解读

Summary We investigate the collinearity of vector time series in the frequency domain, by examining the rank of the spectral density matrix at a given frequency of interest. Rank reduction corresponds to collinearity at the given frequency. When the time series is nonstationary and has been differenced to stationarity, collinearity corresponds to co-integration at a particular frequency. We examine rank through the Schur complements of the spectral density matrix, testing…

动态模型与数据驱动投资组合策略的预测密度组合AI 解读

A dynamic asset-allocation model is specified in probabilistic terms as a combination of return distributions resulting from multiple pairs of dynamic models and portfolio strategies based on momentum patterns in US industry returns. The nonlinear state space representation of the model allows efficient and robust simulation-based Bayesian inference using a novel non-linear filter. Combination weights can be cross-correlated and correlated over time using feedback…

具有非参数先验的多变点面板模型：贝叶斯推断与预测AI 解读

Change point models using hierarchical priors have been very successful estimating the parameter values of short-lived regimes. However, hierarchical priors have been parametric which leads to shrinkage in the estimates of extraordinary regime parameters. We overcome this by modeling the hierarchical priors nonparametrically. We also extend the change point to a panel of change point processes where the prior shares in the probabilities of changing regimes. When applied to…

用马尔可夫转换图结构SUR模型刻画系统性风险AI 解读

We propose a Markov Switching Graphical Seemingly Unrelated Regression (MS-GSUR) model to investigate time-varying systemic risk based on a range of multi-factor asset pricing models. Methodologically, we develop a Markov Chain Monte Carlo (MCMC) scheme in which latent states are identified on the basis of a novel weighted eigenvector centrality measure. An empirical application to the constituents of the S&P100 index shows that cross-firm connectivity significantly…

高维时变协方差的稀疏贝叶斯估计AI 解读

We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stochastic factors. The use of a global–local shrinkage prior for the elements of the factor loadings matrix pulls loadings on superfluous factors towards zero. To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as to daily log-returns of 300 S&P 500…