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利用横截面均值增广时间序列回归进行预测AI 解读

Summary There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much…

时变模型平均AI 解读

Structural changes often occur in economics and finance due to changes in preferences, technologies, institutional arrangements, policies, crises, etc. Improving forecast accuracy of economic time series with structural changes is a long-standing problem. Model averaging aims at providing an insurance against selecting a poor forecast model. All existing model averaging approaches in the literature are designed with constant (non-time-varying) combination weights. Little…

我们能预测多远？预测信息含量的统计检验AI 解读

Summary We develop tests for the null hypothesis that forecasts become uninformative beyond some maximum forecast horizon h ∗ . The forecast may result from a survey of forecasters or from an estimated parametric model. The first class of tests compares the mean‐squared prediction error of the forecast to the variance of the evaluation sample, whereas the second class of tests compares it with the mean‐squared prediction error of the recursive mean. We show that the forecast…

随机缺失下的因子模型：EM 估计、推断与交叉验证AI 解读

We consider the estimation and inference in approximate factor models with random missing values. We show that with the low rank structure of the common component, we can estimate the factors and factor loadings consistently with the missing values replaced by zeros. We establish the asymptotic distributions of the resulting estimators and those based on the EM algorithm. We also propose a cross-validation-based method to determine the number of factors in factor models with…

贝叶斯MIDAS惩罚回归：估计、选择与预测AI 解读

We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian estimation and inference. In particular, to improve the prediction properties of the model and its sparse recovery ability, we consider a Group Lasso with a spike-and-slab prior. Penalty hyper-parameters governing the model shrinkage are automatically tuned via an adaptive MCMC algorithm. We establish good frequentist asymptotic…

增广因子模型：市场风险因子验证与债券风险溢价预测AI 解读

We study factor models augmented by observed covariates that have explanatory powers on the unknown factors. In financial factor models, the unknown factors can be reasonably well explained by a few observable proxies, such as the Fama–French factors. In diffusion index forecasts, identified factors are strongly related to several directly measurable economic variables such as consumption-wealth variable, financial ratios, and term spread. With those covariates, both the…

误设模型的广义聚合：资产定价应用AI 解读

We propose a generalized aggregation approach for model averaging. The entropy-based optimal criterion is a natural choice for aggregating information from many “globally” misspecified models as it adapts better to the underlying model uncertainty and obtains more robust approximations. Unlike almost all other approaches in the existing literature, we do not require a “reference model,” or a true data generation process contained in the set of models — neither implicitly nor…

日本股票市场的尾部风险与收益可预测性AI 解读

This paper studies the predictability of the Japanese equity market, focusing on the forecasting power of nonparametric volatility and tail risk measures obtained from options data on the S&P 500 and Nikkei 225 market indices. The Japanese market is notoriously difficult to forecast using standard predictive indicators. We confirm that country-specific regressions for Japan – contrary to existing evidence for other national equity indices – produce insignificant…

人口特征与社交距离指标在美国COVID-19病例深度预测中的作用AI 解读

With the severity of the COVID-19 outbreak, we characterize the nature of the growth trajectories of counties in the United States using a novel combination of spectral clustering and the correlation matrix. As the United States and the rest of the world are still suffering from the effects of the virus, the importance of assigning growth membership to counties and understanding the determinants of the growth is increasingly evident. For the two communities (faster versus…

局部平稳时间序列的预测推断及气候数据应用AI 解读

The model-free prediction principle of Politis has been successfully applied to general regression problems, as well as problems involving stationary time series. However, with long time series, for example, annual temperature measurements spanning over 100 years or daily financial returns spanning several years, it may be unrealistic to assume stationarity throughout the span of the dataset. In this article, we show how model-free prediction can be applied to handle time…

利用消息传递算法进行高维宏观经济预测AI 解读

This article proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility, and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either…

基于贝叶斯模型平均的半参数 GARCHAI 解读

As the dynamic structure of financial markets is subject to dramatic change, a model capable of providing consistently accurate volatility estimates should not make rigid assumptions on how prices change over time. Most volatility models impose a particular parametric functional form that relates an observed price change to a volatility forecast (news impact function). Here, a new class of functional coefficient semiparametric volatility models is proposed, where the news…

锚回归：异质数据与因果性AI 解读

Abstract We consider the problem of predicting a response variable from a set of covariates on a data set that differs in distribution from the training data. Causal parameters are optimal in terms of predictive accuracy if in the new distribution either many variables are affected by interventions or only some variables are affected, but the perturbations are strong. If the training and test distributions differ by a shift, causal parameters might be too conservative to…

共轭向量自回归模型中收缩与稀疏性的结合AI 解读

Summary Conjugate priors allow for fast inference in large dimensional vector autoregressive (VAR) models. But at the same time, they introduce the restriction that each equation features the same set of explanatory variables. This paper proposes a straightforward means of postprocessing posterior estimates of a conjugate Bayesian VAR to effectively perform equation‐specific covariate selection. Compared with existing techniques using shrinkage alone, our approach combines…

DSGE 模型的在线估计AI 解读

Summary This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating dynamic stochastic general equilibrium (DSGE) model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for ‘online’ estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that are well captured by SMC…

近单位根自回归中的广义预测平均AI 解读

Summary This paper develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging. Within a local-to-unity asymptotic framework, we derive analytical expressions for the asymptotic mean squared error and one-step-ahead mean squared forecast risk of the proposed estimator and show that the optimal FGLS weights are different from…

稀疏单方程协整建模的自动化方法AI 解读

In this paper we propose the Single-equation Penalized Error Correction Selector (SPECS) as an automated estimation procedure for dynamic single-equation models with a large number of potentially (co)integrated variables. By extending the classical single-equation error correction model, SPECS enables the researcher to model large cointegrated datasets without necessitating any form of pre-testing for the order of integration or cointegrating rank. Under an asymptotic regime…

乘法误差模型中基于自助法的概率预测AI 解读

As evidenced by an extensive empirical literature, multiplicative error models (MEM) show good performance in capturing the stylized facts of nonnegative time series; examples include, trading volume, financial durations, and volatility. This paper develops a bootstrap based method for producing multi-step-ahead probability forecasts for a nonnegative valued time-series obeying a parametric MEM. In order to test the adequacy of the underlying parametric model, a class of…

时变参数随机波动率入均值模型中的动态收缩AI 解读

Summary Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this article, we modify the stochastic volatility in mean (SVM) model by introducing state‐of‐the‐art shrinkage techniques that allow for time variation in the degree of shrinkage. Using a real‐time inflation forecast exercise, we show that employing more flexible…

含 MIDAS 协变量的动态面板：非线性、估计与拟合AI 解读

This paper introduces Mixed Data Sampling (MIDAS) into the panel data context. To address the unidentified nuisance parameter problem, we propose to invert model specification tests for inference on the MIDAS parameter along with bounds tests for model coefficients. Illustrative identification, simulation and empirical analyses are conducted in the dynamic GMM framework. Our framework allows for departures from i.i.d errors such as clustering and dynamic specifications. A…

预测 VIX 与波动率风险溢价：短期融资利差波动因子的作用AI 解读

This paper presents an innovative approach to extract Volatility Factors which predict the VIX, the S&P500 Realized Volatility (RV) and the Variance Risk Premium (VRP). The approach is innovative along two different dimensions, namely: (1) we extract Volatility Factors from panels of filtered volatilities — in particular large panels of univariate ARCH-type models and propose methods to estimate common Volatility Factors in the presence of estimation error and (2) we price…

近单位根回归变量下的近最优长期可预测性检验AI 解读

We develop a method for long-run predictability testing in series Y by a persistent series X. We consider a class of tests based on the long-run behavior of these series that are robust to short-run dynamics and attempt to attain the highest possible power. The test is based on the Whittle approximation to the likelihood ratio that is adjusted to remain accurate across a range of persistence in X . We verify the properties of this test in small simulations and compare this…

潜变量非参数协整回归AI 解读

This article studies the asymptotic properties of empirical nonparametric regressions that partially misspecify the relationships between nonstationary variables. In particular, we analyze nonparametric kernel regressions in which a potential nonlinear cointegrating regression is misspecified through the use of a proxy regressor in place of the true regressor. Such models occur in linear and nonlinear regressions where the regressor suffers from measurement error or where…

基于智能电表数据的分层电力需求概率预测AI 解读

Decisions regarding the supply of electricity across a power grid must take into consideration the inherent uncertainty in demand. Optimal decision-making requires probabilistic forecasts for demand in a hierarchy with various levels of aggregation, such as substations, cities, and regions. The forecasts should be coherent in the sense that the forecast of the aggregated series should equal the sum of the forecasts of the corresponding disaggregated series. Coherency is…