论文检索

住房市场繁荣与衰退期间边际消费倾向是否改变？AI 解读

Summary We extend a widely used semi‐structural model to identify and estimate dynamic consumption elasticities with respect to transitory income shocks. Applying our model to household survey data, we find a structural break in marginal propensities to consume following the end of the housing market boom, with the average across households increasing significantly. There is important heterogeneity by different household balance sheet characteristics, and the increase in the…

得分驱动资产定价：基于截面模型表现预测时变风险溢价AI 解读

This paper proposes a new parametric approach for estimating linear factor pricing models with dynamic risk premia. Time-varying risk prices and exposures follow an observation-driven updating scheme that reduces the one-step-ahead prediction error from a cross-sectional factor model at the current observation. This agnostic approach is particularly useful in situations where predictors are unknown or of uncertain quality. Updating schemes for elliptically distributed…

计量经济学中的时变参数：编辑前言AI 解读

The Themed Issue Time-Varying Parameters in Econometrics consists of eight papers where theoretical, methodological and empirical developments are reported. Particular attention is given to observation-driven time-varying parameter models of which score-driven models is a sub-class. We will introduce the Themed Issue and review its contributions.

高维数据的时变预测组合AI 解读

In this paper, we propose a new nonparametric estimator of time-varying forecast combination weights. When the number of individual forecasts is small, we study the asymptotic properties of the local linear estimator. When the number of candidate forecasts exceeds or diverges with the sample size, we consider penalized local linear estimation with the group SCAD penalty. We show that the estimator exhibits the oracle property and correctly selects relevant forecasts with…

时变风险溢价下用于股票收益预测的惩罚两步回归AI 解读

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no-arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks…

含特征驱动与特异 beta 的因子模型统一预测推断AI 解读

This paper investigates the impact of allowing for characteristic-based time-varying factor betas on the diffusion-index type forecasts. The factor beta consists of two distinct components: the “instrumental beta” is a function of some observable characteristics, while the “idiosyncratic beta” captures more volatile residual movements. To estimate these characteristic-based time-varying betas and the corresponding factors, we apply the projected principal component analysis…

多个分位数的半参数建模AI 解读

We develop a semiparametric model to track a large number of quantiles of a time series. The model satisfies the condition of non-crossing quantiles and the defining property of fixed quantiles. A key feature of the specification is that the updating scheme for time-varying quantiles at each probability level is based on the gradient of the check loss function. Theoretical properties of the proposed model are derived such as weak stationarity of the quantile process and…

多变量面板数据的动态聚类AI 解读

We propose a dynamic clustering model for uncovering latent time-varying group structures in multivariate panel data. The model is dynamic in three ways. First, the cluster location and scale matrices are time-varying to track gradual changes in cluster characteristics over time. Second, all units can transition between clusters based on a Hidden Markov model (HMM). Finally, the HMM’s transition matrix can depend on lagged time-varying cluster distances as well as economic…

具有状态依赖性的预测表现评估AI 解读

We propose a novel forecast evaluation methodology to assess models’ absolute and relative forecasting performance when it is a state-dependent function of economic variables. In our framework, the forecasting performance, measured by a forecast error loss function, is modeled via a hard or smooth threshold model with unknown threshold values. Existing tests either assume a constant out-of-sample forecast performance or use non-parametric techniques robust to time-variation;…

CRPS 学习：按时间与分位数自适应组合预测AI 解读

Combination and aggregation techniques can significantly improve forecast accuracy. This also holds for probabilistic forecasting methods where predictive distributions are combined. There are several time-varying and adaptive weighting schemes such as Bayesian model averaging (BMA). However, the quality of different forecasts may vary not only over time but also within the distribution. For example, some distribution forecasts may be more accurate in the center of the…

动态条件特征值 GARCHAI 解读

In this paper we introduce a multivariate generalized autoregressive conditional heteroskedastic (GARCH) class of models with time-varying conditional eigenvalues. The dynamics of the eigenvalues is derived for the cases with nderlying Gaussian and Student’s t-distributed innovations based on the general theory of dynamic conditional score models by Creal, Koopman and Lucas (2013) and Harvey (2013). The resulting time-varying eigenvalue GARCH models – labeled ‘λ-GARCH’…

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…

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…

分析多维数字表型的混合响应状态空间模型AI 解读

Digital technologies (e.g., mobile phones) can be used to obtain objective, frequent, and real-world digital phenotypes from individuals. However, modeling these data poses substantial challenges since observational data are subject to confounding and various sources of variabilities. For example, signals on patients' underlying health status and treatment effects are mixed with variation due to the living environment and measurement noises. The digital phenotype data thus…

容纳风险估计中的时变异质性：Cox 模型下的迁移学习AI 解读

Transfer learning has attracted increasing attention in recent years for adaptively borrowing information across different data cohorts in various settings. Cancer registries have been widely used in clinical research because of their easy accessibility and large sample size. Our method is motivated by the question of how to utilize cancer registry data as a complement to improve the estimation precision of individual risks of death for inflammatory breast cancer (IBC)…

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…

基于常微分方程的生存分析AI 解读

This article introduces an Ordinary Differential Equation (ODE) notion for survival analysis. The ODE notion not only provides a unified modeling framework, but more importantly, also enables the development of a widely applicable, scalable, and easy-to-implement procedure for estimation and inference. Specifically, the ODE modeling framework unifies many existing survival models, such as the proportional hazards model, the linear transformation model, the accelerated…

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…

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…

时变系数合成控制：结合贝叶斯收缩的状态空间方法AI 解读

Synthetic control methods are a popular tool for measuring the effects of policy interventions on a single treated unit. In practice, researchers create a counterfactual using a linear combination of untreated units that closely mimic the treated unit. Oftentimes, creating a synthetic control is not possible due to untreated units’ dynamic characteristics such as integrated processes or a time varying relationship. These are cases in which viewing the counterfactual…

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…

局部平坦函数系数回归的极限理论AI 解读

Functional coefficient (FC) regressions allow for systematic flexibility in the responsiveness of a dependent variable to movements in the regressors, making them attractive in applications where marginal effects may depend on covariates. Such models are commonly estimated by local kernel regression methods. This paper explores situations where responsiveness to covariates is locally flat or fixed. The paper develops new asymptotics that take account of shape characteristics…

部分线性单指标变系数模型的两阶段估计与偏差校正经验似然AI 解读

Abstract The estimation and empirical likelihood (EL) of the parameters of interest in a partially linear single-index varying-coefficient model are studied. A two-stage method is presented to estimate the regression parameters and the coefficient functions. The asymptotic distributions of the proposed estimators are obtained. Meanwhile, a bias-corrected EL ratio for the regression parameters is proposed. It is shown that the ratio is asymptotically standard chi-squared. The…