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参数位于参数空间边界时的惩罚准似然估计与模型选择AI 解读

Summary We consider here penalized likelihood-based estimation and model selection applied to econometric time series models, which allow for nonnegativity (boundary) constraints on some or all of the parameters. We establish that joint model selection and estimation result in standard asymptotic Gaussian distributed estimators. The results contrast with nonpenalized estimation, which, as is well-known, leads to nonstandard asymptotic distributions that depend on the unknown…

针对原因特异绝对风险与生存曲线的一步目标最大似然估计AI 解读

Summary This paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent…

用于评估自回归过程处理效应的动态合成控制法AI 解读

Abstract Motivated by evaluating the effects of air pollution alerts on air quality, we propose the dynamic synthetic control method for micro-level data with time-varying confounders and spatial dependence under an auto-regressive model setting. We employ the empirical likelihood to define the synthetic control weights, which ensures a unique solution and permits theoretical analysis. The dynamic matching increases the feasibility of matching and enables us to assess the…

重新审视免带宽推断：样本分割方法AI 解读

Abstract The bandwidth-free tests for a multi-dimensional parameter have attracted considerable attention in econometrics and statistics literature. These tests can be conveniently implemented due to their tuning-parameter free nature and possess more accurate size as compared to the traditional heteroskedasticity and autocorrelation consistent-based approaches. However, when sample size is small/medium, these bandwidth-free tests exhibit large size distortion when both the…

基于 l₁ 球先验的贝叶斯推断：用精确零值求解组合问题AI 解读

Abstract The l1-regularisation is very popular in high-dimensional statistics—it changes a combinatorial problem of choosing which subset of the parameter is zero, into a simple continuous optimisation. Using a continuous prior concentrated near zero, the Bayesian counterparts are successful in quantifying the uncertainty in the variable selection problems; nevertheless, the lack of exact zeros makes it difficult for broader problems such as change-point detection and rank…

基于深度神经网络的图像响应回归AI 解读

Abstract Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it…

时变最小方差投资组合AI 解读

This paper proposes a new time-varying minimum variance portfolio (TV-MVP) in a large investment universe of assets. Our method extends the existing literature on minimum variance portfolios by allowing for time-varying factor loadings, which facilitates the capture of the dynamics of the covariance structure of asset returns (and hence, the optimal investment strategy in a dynamic setting). We also use a shrinkage estimation method based on a quasi-likelihood function to…

结构变化检测中控制错误发现率的广义Knockoff程序AI 解读

Controlling false discovery rate (FDR) is crucial for variable selection, multiple testing, among other signal detection problems. In literature, there is certainly no shortage of FDR control strategies when selecting individual features, but the relevant works for structural change detection, such as profile analysis for piecewise constant coefficients and integration analysis with multiple data sources, are limited. In this paper, we propose a generalized knockoff…

长期月度温度序列与向量季节均值和协方差变动自回归模型AI 解读

We consider a vector version of the Shifting Seasonal Mean Autoregressive model. The model is used for describing dynamic behaviour of and contemporaneous dependence between a number of long monthly temperature series for 20 cities in Europe, extending from the second half of the 18th century until mid-2010s. The results indicate strong warming in the winter months, February excluded, and cooling followed by warming during the summer months. Error variances are mostly…

线性时变系数模型的筛自助法推断AI 解读

We propose a sieve bootstrap framework to conduct pointwise and simultaneous inference for time-varying coefficient regression models based on a local linear estimator. The asymptotic validity of the sieve bootstrap in the presence of autocorrelation is established. The bootstrap automatically produces a consistent estimate of nuisance parameters, both at the interior and boundary points. In addition, we develop a bootstrap-based test for parameter constancy and examine its…

用于时变网络的贝叶斯马尔可夫切换张量回归AI 解读

Modeling time series of multilayer network data is challenging due to the peculiar characteristics of real-world networks, such as sparsity and abrupt structural changes. Moreover, the impact of external factors on the network edges is highly heterogeneous due to edge- and time-specific effects. Capturing all these features results in a very high-dimensional inference problem. A novel tensor-on-tensor regression model is proposed, which integrates zero-inflated logistic…

采用收缩先验的时变参数动态二元 Probit 模型AI 解读

This article studies a time series binary probit model in which the underlying latent variable depends on its lag and exogenous regressors. The regression coefficients for the latent variable are allowed to vary over time to capture possible model instability. Bayesian shrinkage priors are applied to automatically differentiate fixed and truly time-varying coefficients and thus avoid unnecessary model complexity. I develop an MCMC algorithm for model estimation that exploits…

时变 VAR 模型的估计、推断与实证分析AI 解读

Vector autoregressive (VAR) models are widely used in practical studies, for example, forecasting, modeling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this article introduces a new class of time-varying VAR models in which the coefficients and covariance matrix of the error innovations are allowed to change smoothly over time. Accordingly, we establish a set of asymptotic properties including the impulse…

双变量时变价格停滞研究AI 解读

Price staleness refers to the extent of zero returns in price dynamics. Bandi, Pirino, and Reno introduce two types of staleness: systematic and idiosyncratic staleness. In this study, we allow price staleness to be time-varying and study the statistical inference for idiosyncratic and common price staleness between two assets. We propose consistent estimators for both time-varying idiosyncratic and systematic price staleness and derive their asymptotic theory. Moreover, we…

多门限变量自回归模型的最小二乘估计AI 解读

Most threshold models to-date contain a single threshold variable. However, in many empirical applications, models with multiple threshold variables may be needed and are the focus of this article. For the sake of readability, we start with the Two-Threshold-Variable Autoregressive (2-TAR) model and study its Least Squares Estimation (LSE). Among others, we show that the respective estimated thresholds are asymptotically independent. We propose a new method, namely the…

时变因子模型的识别AI 解读

The emergence of large datasets with long time spans has cast doubt on the assumption of constant loadings in conventional factor models. Being a potential solution, the time-varying factor model (TVFM) has attracted enormous interest in the literature. However, TVFM also suffers from the well-known problem of nonidentifiability. This article considers the situations under which both the factors and factor loadings can be estimated without rotations asymptotically.…

线性回归模型中结构突变位置的估计AI 解读

This study proposes a point estimator of the break location for a one-time structural break in linear regression models. If the break magnitude is small, the least-squares estimator of the break date has two modes at the ends of the finite sample period, regardless of the true break location. To solve this problem, I suggest an alternative estimator based on a modification of the least-squares objective function. The modified objective function incorporates estimation…

利用矩条件对时变参数进行观测驱动滤波AI 解读

We develop a new and flexible semi-parametric approach for time-varying parameter models when the true dynamics are unknown. The time-varying parameters are estimated using a recursive updating scheme that is driven by the influence function of a conditional moments-based criterion. We show that the updates ensure local improvements of the conditional criterion function in expectation. The dynamics are observation driven, which yields a computationally efficient methodology…

因子模型结构变化的似然比检验AI 解读

A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but with a change in the variance of its factors. This approach effectively transforms a high-dimensional structural change problem into a low-dimensional problem. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly explores a special feature of the estimated factors: the pre-break…

自回归条件贝塔模型AI 解读

This paper introduces an autoregressive conditional beta (ACB) model that allows regressions with dynamic betas (or slope coefficients) and residuals with GARCH conditional volatility. The model fits in the (quasi) score-driven approach recently proposed in the literature, and it is semi-parametric in the sense that the distributions of the innovations are not necessarily specified. The time-varying betas are allowed to depend on past shocks and exogenous variables. We…

非平稳条件下 HAR 检验的 fixed-b 极限分布与拒绝概率误差AI 解读

We show that the limiting distribution of HAR test statistics under fixed-b asymptotics is not pivotal when the data are nonstationary (i.e., time-varying autocovariance structure). It takes the form of a complicated function of Gaussian processes and depends on the second moments of the relevant series (e.g., of the regressors and errors for the case of the linear regression model). Hence, fixed-b inference methods based on stationarity are not theoretically valid in…

结构突变的 KS 型检验：基于调整极差的自归一化方法AI 解读

A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorrelation, heteroskedasticity, unit roots and outliers. We propose a novel SN approach based on the adjusted-range of a partial sum, which is robust to these aforementioned irregularities. We develop an adjusted-range based Kolmogorov–Smirnov type test for structural breaks for…

年轻女性的心理健康与人工流产：时变未观测异质性、健康行为和风险决策AI 解读

In this paper, we provide causal evidence on abortions and risky health behaviors as determinants of mental health development among young women. Using administrative in- and outpatient records from Sweden, we apply a novel grouped fixed-effects estimator proposed by Bonhomme and Manresa (2015) to allow for time-varying unobserved heterogeneity. We show that the positive association obtained from standard estimators shrinks to zero once we control for grouped unobserved…

正态混合误差下非平稳位置模型的极大似然估计AI 解读

We consider an observation-driven location model where the unobserved location variable is modeled as a random walk process and where the error variable is from a mixture of normal distributions. The time-varying location can be extended with a stationary process to account for cyclical and/or higher order autocorrelation. The mixed normal distribution can accurately approximate many continuous error distributions. We obtain a flexible modeling framework for the robust…