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贝叶斯动态矩阵因子模型AI 解读

The growth in data volume and the expansion in data dimensionality are challenging the analysis of high-dimensional matrix time series. Factor models for matrix-valued high-dimensional time series are a powerful tool for reducing the dimensionality of the variables with low-rank structures. However, existing high-dimensional matrix factor models are nearly all static and struggle to capture the dynamics and time evolution of data. In this article, a dynamic matrix factor…

广义自回归条件贝塔：多因子资产定价中的纵向反馈AI 解读

We propose a new class of observation-driven models, Generalized Autoregressive Conditional Betas, which describe the joint dynamics of time-varying slopes in a system of conditionally heteroskedastic simultaneous multiple regressions. The model accommodates large dimensions, parametric longitudinal restrictions, exogenous variables, and the coexistence of constant and time-varying slopes. It also introduces new mechanisms for the transmission of shocks, namely, beta…

因子网络自回归AI 解读

We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents (“multilayer network”), which are summarized into a smaller number of network matrices (“network factors”) through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the factors, their loadings, and…

避免代理变量 VAR 分析中非预期的冲击相关性AI 解读

Noting that the shocks in vector autoregressive models can be correlated if a number of shocks is identified individually by multiple proxy variables, we propose a Generalized Method of Moments (GMM) approach for estimation that enforces uncorrelated shocks.We point out that if each proxy identifies exactly one shock and is uncorrelated with all other shocks, uncorrelatedness of the shocks provides over-identifying restrictions that can be used in our approach to improve the…

高维弱工具变量下的推断与新凯恩斯菲利普斯曲线AI 解读

Inference on macroeconomic relations using instrumental variables (IVs) is often characterised by weak and high-dimensional IVs. I propose a weak-IV robust method that remains valid under dependent data, arbitrarily weak identification, and a number of IVs that can increase exponentially with the sample size. I show by simulation that using methods that are not robust to weak and high-dimensional IVs can lead to substantially biased inference, while the proposed test has…

条件得分残差与时间序列模型的序列依赖诊断AI 解读

This paper introduces conditional score residuals and proposes a general framework for the diagnostic analysis of time series models. Conditional score residuals encompass commonly used definitions of residuals in time series models, including ARMA residuals, squared residuals, and Pearson residuals. In particular, these residuals are special cases of conditional score residuals when the conditional distribution of the model belongs to the exponential family. On the other…

金融时间序列截面最大值的自回归条件 GB2 动态建模AI 解读

This article introduces the “autoregressive conditional generalized beta distribution of the second kind” (AcGB2) to model the dynamic cross-sectional maxima of multivariate financial times series. The temporal dependence of the resulting univariate maxima time series is characterized by the parameter dynamics of the standard GB2 distribution, which offer versatility in approximating various distributions, including heavy-tailed distributions, through parameter adjustments.…

半参数时变面板数据模型的估计与推断AI 解读

This article introduces a new semiparametric panel data model that accounts for time-varying coefficients and aligns with recent advancements in factor models featuring nonparametric loading functions. We propose a profile marginal integration (PMI) method to jointly estimate the unknown quantities in a series of easily implementable steps. The asymptotic properties of these estimators are established. Additionally, we provide a hypothesis test to assess the validity of…

基于张量分解的向量自回归贝叶斯推断AI 解读

Vector autoregression (VAR) is a popular model for analyzing multivariate economic time series. However, VARs can be over-parameterized if the numbers of variables and lags are moderately large. Tensor VAR, a recent solution to over-parameterization, treats the coefficient matrix as a third-order tensor and estimates the corresponding tensor decomposition to achieve parsimony. In this paper, we employ the Tensor VAR structure with a CANDECOMP/PARAFAC (CP) decomposition and…

精度矩阵最小二乘：高维估计与推断AI 解读

The least squares estimator can be cast as depending only on the precision matrix of the data, similar to the weights of a global minimum variance portfolio. We give conditions under which any plug-in precision matrix estimator produces an unbiased and consistent least squares estimator for stationary time series regressions, in both low- and high-dimensional settings. Such conditions define a class of “Precision Least Squares” (PrLS) estimators, which are shown to be…

资产收益中因子强度的不稳定性AI 解读

We study the problem of detecting structural instability of factor strength in asset pricing models for financial returns with observable factors.We allow for strong and weaker factors, in which the sum of squared betas grows at a rate equal to and slower than the number of test assets, respectively: this growth rate determines the strength of the corresponding factor.We propose LM and Wald statistics for the null hypothesis of stability and derive their asymptotic…

观察性研究中多值处理政策学习的模型选择AI 解读

This study investigates the policy learning problem in observational studies, where the treatment variable can be multivalued and the propensity scores are unknown. We approximate the optimal policy in a global policy class with infinite complexity (VC/Natarajan) dimension, using a sequence of sieve policy classes with finite complexity dimension. The optimal policy within each sieve class is estimated by maximizing the empirical welfare, constructed through the doubly…

长期方差的正定收敛核估计AI 解读

Kernel estimators have been popular for decades in long-run variance estimation.To minimize the loss of efficiency measured by the mean-squared error in important aspects of kernel estimation, we propose a novel class of converging kernel estimators with three major properties: (a) the optimal bandwidth choice is model-free; (b) positive-definiteness is ensured through a principle-driven aggregation technique with no loss of theoretical efficiency; and (c) potentially…

去偏 Lasso 的小调参选择AI 解读

In this study, we investigate the bias and variance properties of the debiased Lasso in linear regression when the tuning parameter of the node-wise Lasso is selected to be smaller than in previous studies. We consider the case where the number of covariates p is bounded by a constant multiple of the sample size n. First, we show that the bias of the debiased Lasso can be reduced without diverging the asymptotic variance by setting the order of the tuning parameter to 1/n.…

生产者—消费者模型识别与推断的显示偏好方法AI 解读

This paper provides a new identification result for a large class of models in which consumers participate in production. I show that consumer preferences are necessary and sufficient to identify production functions through cross-equation restrictions implied by first-order conditions. In addition, I derive a nonparametric revealed preference characterization of the class of models that exhausts its empirical implications. Finally, I use a novel and easy-to-apply inference…

微观结构噪声下傅里叶瞬时波动率估计的渐近正态性与有限样本稳健性AI 解读

We study the efficiency and robustness of the Fourier spot volatility estimator when high-frequency prices are contaminated by microstructure noise. First, we show that the estimator is consistent and asymptotically efficient in the presence of additive noise, establishing a Central Limit Theorem (CLT) with the optimal rate of convergence n1/8. Additionally, we complete the asymptotic theory in the absence of noise, obtaining a CLT with the optimal rate of convergence n1/4.…

极端连续处理效应的估计与推断AI 解读

This article studies estimation and inference for the treatment effect in deep tails of the potential outcome distributions corresponding to a continuously valued treatment, namely the extreme continuous treatment effect. We consider two measures for the tail characteristics: the quantile function and the tail mean function defined as the conditional mean beyond a quantile level. Then, for a quantile level close to 1, we define the extreme quantile treatment effect (EQTE)…

裂区设计中的再随机化与协变量调整AI 解读

Split-plot designs are widely used in agricultural, industrial, social, and biomedical experiments to accommodate hard-to-change factors. Given multiple factors of interest and experimental units that are nested within groups, the design assigns a subset of the factors by a cluster randomization at the group level and assigns the remaining factors by a stratified randomization at the unit level. The randomization mechanism ensures covariate balance on average at both the…

交叉验证后自助法选择高维 M 估计量的惩罚参数AI 解读

We develop a new method for selecting the penalty parameter for ℓ1-penalized M-estimators in high dimensions, which we refer to as bootstrapping after cross validation. We derive rates of convergence for the corresponding ℓ1-penalized M-estimator and also for the post-ℓ1-penalized M-estimator, which refits the nonzero entries of the former estimator without penalty in the criterion function. We demonstrate via simulations that our methods are not dominated by cross…

函数值空间过程的谱密度估计AI 解读

The spectral density function describes the second-order properties of a stationary stochastic process on Rd. This paper considers the nonparametric estimation of the spectral density of a continuous-time stochastic process taking values in a separable Hilbert space. Our estimator is based on kernel smoothing and can be applied to a wide variety of spatial sampling schemes including those in which data are observed at irregular spatial locations. Thus, it finds immediate…

通过神经对抗不变性学习从异质环境中追寻因果关系AI 解读

Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for addressing nonparametric invariance and causality learning in regression models across multiple environments, where the joint distribution of response variables and covariates varies, but the conditional expectations of outcome given an unknown set of quasi-causal variables are invariant. The…

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基于样本协方差矩阵的完全随机缺失检验AI 解读

We study the problem of testing whether the missing values of a potentially high-dimensional dataset are Missing Completely at Random (MCAR). We relax the problem of testing MCAR to the problem of testing the compatibility of a collection of covariance matrices, motivated by the fact that this procedure is feasible when the dimension grows with the sample size. Our first contributions are to define a natural measure of the incompatibility of a collection of correlation…

It is well-known that trimmed sample means are robust against heavy tails and data contamination. This paper analyzes the performance of trimmed means and related methods in two novel contexts. The first one consists of estimating expectations of functions in a given family, with uniform error bounds; this is closely related to the problem of estimating the mean of a random vector under a general norm. The second problem considered is that of regression with quadratic loss.…

强化学习中稳健策略评估的在线估计与推断AI 解读

Reinforcement learning has emerged as one of the prominent topics attracting attention in modern statistical learning, with policy evaluation being a key component. Unlike the traditional machine learning literature on this topic, our work emphasizes statistical inference for the model parameters and value functions of reinforcement learning algorithms. While most existing analyses assume random rewards to follow standard distributions, we embrace the concept of robust…