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以倾向得分校准改善双重／去偏机器学习的平均处理效应有限样本估计AI 解读

ABSTRACT Double/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double‐robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates. Estimators relying on double‐robust score functions are highly sensitive to errors in propensity score predictions. Machine learning algorithms have been found to produce models that often…

因子增强回归预测准确性的稳健检验：新欧元区 EA-MD-QD 数据集应用AI 解读

ABSTRACT We present four novel tests of equal predictive accuracy and encompassing á Pitarakis (2023, 2025) for factor‐augmented regressions. Factors are estimated using cross‐section averages (CAs) of grouped series and our theoretical findings are empirically relevant: asymptotic normality, robustness to an overspecification of the number of factors, tractability of different degrees of predictor persistence, and invariance to the location of structural breaks in the…

高频信息组合优化中重审 EWMA：比较评估AI 解读

ABSTRACT This paper compares the statistical and economic performance of state‐of‐the‐art high‐frequency (HF) based multivariate volatility models with a simpler, widely used alternative, the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S. stock returns (2002–2023), we assess model performance through a Global Minimum Variance portfolio optimization exercise, with and without short‐selling restrictions across multiple forecast…

广义自回归得分树与森林AI 解读

We propose methods to improve the forecasts from generalized autoregressive score (GAS) models (Creal, et al., 2013; Harvey, 2013) by localizing their parameters using decision trees and random forests. These methods avoid the curse of dimensionality faced by kernel-based approaches, and allow one to draw on information from multiple state variables simultaneously. We apply the new models to four distinct empirical analyses, and in all applications the proposed new methods…

预测组合：为什么简单平均胜过最优线性权重AI 解读

A continuing puzzle in constructing a point forecast by combining individual forecasts is that simple averaging often leads to a smaller out-of-sample mean square error compared to estimating optimal weights (the forecast combination puzzle). Most researchers have suggested that the puzzle is explained by estimation error in computing the weights, typically by estimation of a restricted least squares regression. For this explanation, gains from optimal weights must be small.…

任意带宽选择下的预测模型权重形状优化AI 解读

Parameter instability has been widely documented to lead to poor predictive accuracy for economic forecasting models. Adaptive schemes for data weighting, such as rolling-window estimation, only involve a single tuning parameter (akin to a bandwidth) and have been widely used to address this issue. The importance of the shape of the weighting scheme (kernel) has received far less attention in the forecasting literature. In this paper we show that in practice there can be a…

利用预测识别新闻冲击AI 解读

ABSTRACT We propose a method to identify the anticipated components of macroeconomic shocks in a structural VAR. We include empirical forecasts about each time series in the VAR. This introduces enough linear restrictions to identify every structural shock and to further decompose each one into “news” and “surprise” shocks. We estimate a VAR on U.S. time series using forecast data from the SPF, CBO, Federal Reserve, and asset prices. Unanticipated fiscal stimulus and…

因子增强稀疏 MIDAS 回归及其即时预测应用AI 解读

This article investigates factor-augmented sparse MIDAS (Mixed Data Sampling) regressions for high-dimensional time series data, which may be observed at different frequencies. Our novel approach integrates sparse and dense dimensionality reduction techniques. We derive the convergence rate of our estimator under misspecification due to the MIDAS approximation error, τ-mixing dependence, and polynomial tails. Our method’s finite sample performance is assessed via Monte Carlo…

资产价格能否帮助预测通胀？来自个股价格的证据AI 解读

This paper revisits the predictive power of asset prices for inflation, focusing on individual stock prices rather than aggregate indices. Using a large panel data of firm-level stock prices and applying machine learning techniques, we demonstrate that individual stock prices significantly enhance the accuracy of inflation forecasts, particularly over medium- to long-term horizons and during periods of high inflationary and deflationary pressure. Compared to composite and…

基于误差协方差特征分解的快速、顺序不变 VAR 贝叶斯推断AI 解读

Bayesian inference in Vector Autoregressions (VARs) involves manipulating large matrices which appear in the posterior (or conditional posterior) of the VAR coefficients. For large VARs, the computational burden of these manipulations can render empirical work impractical. In response to this, many researchers transform their VARs so as to allow for Bayesian estimation to proceed one equation at a time. This leads to a massive reduction in the computational burden. This…

重新考察多预测期的一致优越预测能力检验AI 解读

This paper examines the joint-hypothesis-testing problem that arises when comparing two competing forecast methods across multiple horizons. We focus on the concept of uniform Superior Predictive Ability (uSPA) and investigate the asymptotic properties of the corresponding test statistic. Under standard regularity conditions, the asymptotic distribution under the null hypothesis is derived, ensuring that the test maintains the correct size and exhibits consistency. Monte…

将预测能力推断扩展至机器学习方法AI 解读

The use of machine learning methods for predictive purposes has exponentially increased over the past two decades, but uncertainty quantification for predictive comparisons remains challenging and underdeveloped. This article addresses this gap by extending the classic inference theory for predictive ability in time series to modern machine learners, such as Lasso or Deep Learning. We investigate under which conditions such extensions are possible. For standard out-of-sample…

极值蒙特卡洛滤波器：通过模拟与回归提取信号AI 解读

We introduce a novel simulation-based method for signal extraction in a general class of state space models. It can be used to estimate time-varying conditional means, modes, and quantiles, and to predict latent variables or forecast observations. The method consists of generating artificial datasets from the model and estimating the quantities of interest via extremum estimation. The approach is broadly applicable and its implementation is straightforward. The method is…

时间序列长期方差估计中的尾部后着色AI 解读

Prewhitening is a common approach to deal with strong autocorrelation. In this article, we propose a new approach called tail postcoloring, motivated by it. It uses parametric models to project, or color back, the neglected tail autocovariances in nonparametric estimators onto the final estimator. This approach bridges the non-parametric variance estimator and the parametric coloring model through a scaling factor. It automatically switches between these two arms using a…

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双参数泊松–狄利克雷隐马尔可夫模型的准共轭精确推断AI 解读

We introduce a nonparametric model for inferring time-evolving, unobserved probability distributions from discrete-time data consisting of unlabelled partitions. The latent process is a two-parameter Poisson–Dirichlet diffusion, and observations arise via exchangeable sampling. Applications include social and genetic data where only aggregate clustering summaries are observed.To address the intractable likelihood, we develop a tractable inferential framework that avoids…

经济与金融中的贝叶斯方法：统一综述与分类AI 解读

This special issue brings together 21 papers that reflect the expanding scope and methodological evolution of Bayesian econometrics. We introduce a new organizing taxonomy that distinguishes between Traditional Bayesian Approaches - rooted in conjugate priors, standard likelihood-based estimation, and established Markov Chain Monte Carlo (MCMC) techniques - and Contemporary Bayesian Frontiers, characterized by nonparametric methods, variational inference, high-dimensional…

未知形式异方差下非基本 ARMA 模型的时域估计AI 解读

This paper considers time domain estimation of possibly non-fundamental (that is, non-causal and/or non-invertible) non-Gaussian linear ARMA models with martingale difference innovations that may display conditional heteroskedasticity of unknown form. Instead of explicitly parametrizing the underlying volatility process (the higher order dependence) and employing maximum likelihood procedures, we propose a time domain minimum distance objective function based on innovations…

非对称风险下的宏观审慎政策：贝叶斯结构分位数 VAR 方法AI 解读

Macro-prudential policymakers face a risk-management problem: they must assess medium-term downside risks to the real economy stemming from financial imbalances and the associated threat of future financial crises, while accounting for the cost of policy interventions in terms of foregone upside potential. This trade-off between limiting downside risks and preserving upside growth is inherently asymmetric and depends on the distribution of future macroeconomic outcomes. We…

动态状态空间 HAR 模型AI 解读

The Heterogeneous AutoRegressive model for the logs of Realised Volatility (HARL) has established itself as the benchmark specification for modelling and forecasting return volatility, owing to its parsimony and ability to capture the strong persistence typically observed in RV. To address potential concerns such as measurement errors, nonlinearities, and non-spherical residuals, numerous variants of the baseline HARL model have been developed in the literature. This paper…

非随机缺失数据下的共同基金绩效贝叶斯评估：中国市场应用AI 解读

This paper studies mutual fund performance evaluation, where non-random missing data is an important issue. Ignoring sample selection issues can lead to biased parameter estimates and invalid performance evaluation. Therefore, we propose a novel Beta selection model to characterize this non-random missingness mechanism. We can significantly reduce the bias in alpha estimates, by introducing our Beta selection mechanism into the Noise-Reduced Alpha (NRA) model of Harvey and…

贝叶斯模型选择中偏差信息准则的理论依据与应用AI 解读

This paper provides a theoretical justification for the Deviance Information Criterion (DIC) as a Bayesian model selection tool using MCMC output. Unlike Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), which balance model adequacy against complexity without considering prior information, DIC incorporates priors into this trade-off. The contributions of this paper are two-fold. First, it demonstrates that when a plug-in predictive distribution —…

数据丰富环境中的时变完全子集平均AI 解读

This study proposes two novel time-varying model-averaging methods for time-varying parameter regression models. When the number of predictors is small, we propose a novel time-varying complete subset-averaging (TVCSA) procedure, where the optimal time-varying subset size is obtained by minimizing the local leave- h -out cross-validation criterion. The TVCSA method is asymptotically optimal for achieving the lowest possible local mean squared error. When the number of…

相关时间序列的预测AI 解读

ABSTRACT A collection of time series are “related” if they follow similar stochastic processes and/or they are statistically dependent. This paper proposes a related time series (RTS) forecasting model that exploits these relationships. The model's foundation is a set of univariate Gaussian autoregressions, one for each series, which are then augmented to incorporate stochastic volatility, heavy‐tailed innovations, additive outliers, time‐varying parameters and common…

分布向量自回归：刻画宏观与金融依赖AI 解读

ABSTRACT This paper extends the vector autoregression framework by introducing a flexible distributional regression that models multivariate time series without imposing restrictive parametric distribution assumptions. We develop a distributional impulse response function that captures the future effect of distributional disturbances within the system, providing a more detailed view of dynamic heterogeneity. We propose a straightforward estimation method and establish its…