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不稳定环境中的预测选择AI 解读

This paper leverages the time-series properties of forecast loss differences for out-of-sample forecast selection. Our framework predicts the conditional distribution of future loss differences while accommodating for time-contingent unstable forecasting environments. We establish distributional theory to quantify the sampling uncertainty of our predictions, enabling the development of advanced selection rules. Through simulations and an empirical application to inflation…

预期分歧的因子结构AI 解读

We estimate a Bayesian three-dimensional dynamic factor model on the individual forecasts in the Survey of Professional Forecasters. The factors extract the most important dimensions along which disagreement comoves across variables. We interpret our results through a generic dispersed information model. The two most important factors in the data describe disagreement about aggregate supply and demand, respectively. Up until the Great Moderation, supply disagreement was…

对跳跃、微观结构噪声与异步观测稳健的高频方差分析AI 解读

This paper develops the necessary methodology for high frequency ANOVA, focusing on the estimations of idiosyncratic volatility and averaged R-Squared. As a nonlinear and complicated functional of the spot covariance matrix, idiosyncratic volatility estimation poses significant challenges, particularly with jumps, microstructure noise, and asynchronous observation times. Averaged R-Squared, a newly introduced quantity in high frequency econometrics, provides a measure of…

双动态最大 Copula 模型及其金融时间序列应用AI 解读

Accurately modeling time-varying dependence structures is essential for financial market analysis, particularly during periods of market stress. Recognizing that traditional copula models often fail to jointly capture dynamic dependence and tail behavior, this article proposes a double dynamic max-copula (DDMC) model that accommodates time variation while effectively characterizing tail dependence. To estimate the model, we introduce a maximum composite profile likelihood…

保持核算关系的层级时间序列过程失真修正AI 解读

Process distortion, that is, the corruption of time series dynamics, can be present in hierarchical time series subject to aggregation. The presence of seasonal patterns in seasonally adjusted data, known as residual seasonality (RS), is an example of process distortion. In the case of U.S. Gross Domestic Product (GDP) and its sub-aggregates, it is important that there be no RS at monthly or quarterly frequencies, while preserving aggregation relations between the various…

平滑转换区间自回归模型AI 解读

Interval time series (ITS) analysis has important significance econometric analysis, as it contains information about the range of change and the level or trend of economic processes. More importantly, the rich information of interval data can be used for more accurate quantitative estimation and inference. Considering the possible nonlinear characteristics of ITS data, this article introduces a class of smooth transition interval autoregressive (STIAR) models, which…

动态半参数风险模型变化的序贯监测AI 解读

We propose a sequential monitoring scheme to detect changes in dynamic semiparametric risk models that capture Value-at-Risk (VaR) and Expected Shortfall (ES) jointly. The monitoring scheme is based on a gradient-based detector and a boundary function, and a change is detected when the detector crosses the boundary function. We derive the asymptotic limit of the stopping time of detection under the null hypothesis of no change. Monte Carlo simulations show that the proposed…

时变参数 VAR 中与经济理论一致的收缩AI 解读

This article introduces a theory coherent shrinkage prior for Time-Varying Parameter VARs (TVP-VARs). The prior centers the time-varying parameters on a path implied a priori by an underlying economic theory, chosen to describe the dynamics of the macroeconomic variables in system. Leveraging information from conventional economic theory using this prior significantly improves inference precision and forecast accuracy compared to the standard TVP-VAR. In an application, I…

随机权重矩阵与解释变量下空间自回归模型的拟得分匹配估计AI 解读

With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the data with a large size. In addition, when establishing the asymptotic properties of the parameter estimators of the SAR…

包络矩阵自回归模型AI 解读

Matrix-valued data is commonly collected over time in many scientific fields. However, existing methods for handling such data are limited and often suffer from overparametrization. In response, Chen et al. (2021) introduced the matrix autoregressive (MAR) model as an alternative to traditional time series analysis, which relies on vectorization and vector autoregression frameworks. By preserving the original structure of matrices, the MAR model avoids the loss of valuable…

基于奇异值分解与小波阈值的混频时间序列季节调整AI 解读

In this article, we propose a novel seasonal adjustment method that accommodates time series observed at mixed frequencies and possessing possibly multiple abrupt changes in seasonality. We assume that the observed series is a known linear transformation of an underlying high frequency series whose seasonal component can be represented as a matrix with a low rank Singular Value Decomposition (SVD) structure, and the nonseasonal component is difference stationary. The right…

GARCH-MIDAS 模型的自适应组 LASSOAI 解读

This article investigates the variable selection problem in the generalized autoregressive conditional heteroscedasticity-mixed-data sampling (GARCH-MIDAS) model of Engle, Ghysels, and Sohn (Citation2013), and establishes the corresponding theoretical properties along with a feasible computational algorithm. The proposed method is based on a penalized log-partial likelihood with an adaptively group-weighted L2 penalty on the long-term component coefficients. The variable…

可解释的市场微观结构噪声AI 解读

High-frequency financial data are often contaminated by market microstructure effects. In this study, we consider a setting where a portion of the microstructure noise can be explained by observable trading information, referred to as the explicative noise component. To formally analyze this component, we first develop a model-free variable importance measure in the high-frequency setting that quantifies the price impact of subsets of trading variables. Based on the…

分组时变网络向量自回归模型的估计AI 解读

This paper introduces a flexible time-varying network vector autoregressive model framework for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with nonparametrically estimated distance matrix is combined with a ratio criterion…

高维协整的迹检验AI 解读

This paper studies Johansen’s (J. Econom. Dynam. Control 12 (1988) 231–254) trace test for cointegration in high-dimensional data. We show that when both cross-sectional and temporal dimension of the data go to infinity proportionally, the shifted and scaled modified trace statistic converges to a Gaussian random variable. We give explicit formulae for the shift and scale parameters as well as for the mean and variance of the Gaussian limit. Monte Carlo analysis shows…

时变方差下瞬时因果关系的非参数检验AI 解读

This paper proposes a consistent nonparametric test with good sampling properties to detect instantaneous causality between vector autoregressive (VAR) variables with time-varying variances. The new test takes the form of the U -statistic, and has a limiting standard normal distribution under the null. We further show that the test is consistent against any fixed alternatives, and has nontrivial asymptotic power against a class of local alternatives with a rate slower than $T^{-1/2}$…

预测回归中位于参数空间边界的参数AI 解读

We consider bootstrap inference in predictive (or Granger-causality) regressions when the parameter of interest may lie on the boundary of the parameter space, here defined by means of a smooth inequality constraint. For instance, this situation occurs when the definition of the parameter space allows for the cases of either no predictability or sign-restricted predictability. We show that in this context constrained estimation gives rise to bootstrap statistics whose limit…

局部单位根自回归中的系数随机性检验AI 解读

This study proposes a test for coefficient randomness in autoregressive models where the autoregressive coefficient is local to unity, which is empirically relevant given earlier work. Under this specification, we analyze the effect of the correlation between the random coefficient and disturbance on the properties of tests, a matter that remains largely unexplored in the literature. Our analysis reveals that tests proposed in earlier studies can have poor power when the…

未知持续性形式下非线性预测回归的按时间修剪最小二乘AI 解读

Relatively, recent work by Jeganathan (2008, Cowles Foundation Discussion Paper 1649) and Wang (2014, Econometric Theory , 30(3), 509–535) on generalized martingale central limit theorems (MCLTs) implicitly introduces a new class of instrument arrays that yield (mixed) Gaussian limit theory irrespective of the persistence level in the data. Motivated by these developments, we propose a new semiparametric method for estimation and inference in nonlinear predictive regressions…

非平稳性与模型误设引起的低频污染理论：对异方差与自相关稳健推断的影响AI 解读

We establish theoretical results about the low frequency contamination (i.e., long memory effects) induced by general nonstationarity for estimates such as the sample autocovariance and the periodogram, and deduce consequences for heteroskedasticity and autocorrelation robust (HAR) inference. We present explicit expressions for the asymptotic bias of these estimates. We show theoretically that nonparametric smoothing over time is robust to low frequency contamination.…

符号约束 VAR 模型中的常规冲击响应先验AI 解读

ABSTRACT Some studies have expressed concern that the Gaussian‐inverse Wishart–Haar prior typically employed in estimating sign‐identified VAR models may be unintentionally informative about the prior for the structural responses. We discuss what features to look for in this prior in the absence of specific prior information about the responses, building on the notion of weakly informative priors in Gelman et al. (2013), and in the presence of such information. Empirical…

半球神经网络：从被动响应到前瞻性波动建模AI 解读

ABSTRACT We revisit maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context. First, the hemispheres share a common core at the entrance of the network, which accommodates various forms of time variation in the error variance. Second, we introduce a volatility emphasis constraint that…

无限阶面板自回归过程的工具变量估计AI 解读

We consider instrumental variables (IV) estimation of a possibly infinite order dynamic panel autoregressive (AR) process with individual effects. The estimation is based on the sieve AR approximation, with its lag order increasing with sample size. Transforming the variable to eliminate individual effects generates an endogeneity problem, particularly when the time series is only moderately long. IV approaches are useful to obtain well-behaved estimators in panels with…

含未观测异质性的大型面板多重结构突变估计AI 解读

This article proposes a novel algorithm to identify potentially multiple breaks in linear panel data models, while the slope coefficients can have individual heterogeneity and the cross-sectional dimension is allowed to diverge jointly with the time span. The algorithm adopts a shrinkage penalty to detect breaks, and delivers a consistent estimator for the number of breaks and fraction-consistent estimators for break dates. In the presence of latent grouped heterogeneity, we…