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北极海冰何时消失？面积、范围、厚度与体积预测AI 解读

Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change. We provide point, interval, and density forecasts for four measures of Arctic sea ice: area, extent, thickness, and volume. Importantly, we enforce the joint constraint that these measures must simultaneously arrive at an ice-free Arctic. We apply this constrained joint forecast procedure to models relating sea ice to atmospheric carbon dioxide concentration and models relating…

连续时间整数值拖曳过程的推断与预测AI 解读

This paper develops likelihood-based methods for estimation, inference, model selection, and forecasting of continuous-time integer-valued trawl processes. The full likelihood of integer-valued trawl processes is, in general, highly intractable, motivating the use of composite likelihood methods, where we consider the pairwise likelihood in lieu of the full likelihood. Maximizing the pairwise likelihood of the data yields an estimator of the parameter vector of the model,…

仅用一阶滞后建模长记忆AI 解读

Two recent contributions have found conditions for large dimensional networks or systems to generate long memory in their individual components. We build on these and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a…

含缺失数据的高维条件高斯状态空间模型AI 解读

We develop an efficient sampling approach for handling complex missing data patterns and a large number of missing observations in conditionally Gaussian state space models. Two important examples are dynamic factor models with unbalanced datasets and large Bayesian VARs with variables in multiple frequencies. A key observation underlying the proposed approach is that the joint distribution of the missing data conditional on the observed data is Gaussian. Furthermore, the…

大型均值含随机波动率的 VAR 模型AI 解读

Bayesian vector autoregressions with stochastic volatility in both the conditional mean and variance (SVMVARs) are widely used for studying the macroeconomic effects of uncertainty. Despite their popularity, intensive computational demands when estimating such models has constrained researchers to specifying a small number of latent volatilities, and made out-of-sample forecasting exercises impractical. In this paper, we propose an efficient Markov chain Monte Carlo (MCMC)…

基于截面相关面板的即时预测AI 解读

Summary This paper builds a mixed‐frequency panel data model for nowcasting economic variables across many countries. The model extends the mixed‐frequency panel vector autoregression (MF‐PVAR) to allow for heterogeneous coefficients and a multifactor error structure to model cross‐sectional dependence. We propose a modified common correlated effects (CCE) estimation technique which performs well in simulations. The model is applied in two distinct settings: nowcasting gross…

汇率与宏观基本面：来自时变因子载荷的不稳定性证据AI 解读

Summary We examine the relationship between exchange rates and macroeconomic fundamentals using a two‐step maximum likelihood estimator through which we compute time‐varying factor loadings. Factors are obtained as principal components, extracted from vintage macro‐datasets that combine FRED‐MD and OECD databases. Using 14 currencies over 1990–2021, we show that the loadings on the factors vary considerably over time and increase the percentage of explained variation in…

油价不确定性、内生状态转换与通胀锚定AI 解读

Summary Using a novel approach to model regime switching with dynamic feedback and interactions, we extract latent mean and volatility factors in oil price changes. We illustrate how the volatility factor constitutes a useful measure of oil market risk (or oil price uncertainty) for policy makers and analysts as it captures uncertainty not reflected in other economic/financial uncertainty measures. Then, in the context of a VAR, we investigate the role of oil price…

含交互固定效应的异质面板二元响应模型AI 解读

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach,…

惩罚化时变模型平均AI 解读

This paper proposes a new penalized time-varying model averaging method to determine optimal time-varying combination weights for candidate models, which avoids over-fitting and yields sparseness from various potential predictive variables. The asymptotic optimality and convergence rate of the selected weights are derived even when all candidate models are misspecified, and the consistency and normality of the proposed time-varying model averaging estimator are obtained when…

多元随机波动率模型中的宏观预测与变量排序AI 解读

We document five novel empirical findings on the well-known potential ordering drawback associated with the time-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005). First, the ordering does not affect point prediction. Second, the standard deviation of the predictive densities implied by different orderings can differ substantially. Third, the average length of the prediction intervals is also…

构造渐近最优组合预测的模型平均方法AI 解读

We propose a model-averaging (MA) method for constructing an asymptotically optimal combination of a set of point forecast sequences generated from a class of predictive regressions. The asymptotic optimality is defined in terms of approximating an unknown conditional-mean sequence based on the local(-to-zero) asymptotics. Our method has the following essential features. First, it is more general than combining a set of single point forecasts. Second, the asymptotic…

含随机局部单位根预测变量的预测回归稳健推断AI 解读

This paper explores predictive regression models with stochastic unit root (STUR) components and robust inference procedures that encompass a wide class of persistent and time-varying stochastically nonstationary regressors. The paper extends the mechanism of endogenously generated instrumentation known as IVX, showing that these methods remain valid for short and long-horizon predictive regressions in which the predictors have STUR and local STUR (LSTUR) generating…

GARCH 的预测密度与分布泛函预测AI 解读

This paper derives the analytic form of the multi-step ahead prediction density of a Gaussian GARCH(1,1) process with a possibly asymmetric news impact curve in the GJR class. These results can be applied when single-period returns are modeled as a GJR Gaussian GARCH(1,1) and interest lies in single-period returns at some future forecast horizon. The Gaussian density has been used in applications as an approximation to this as yet unknown prediction density; the analytic…

删失密度预测的生成与评估AI 解读

Summary This paper develops methods for the production and evaluation of censored density forecasts. The focus is on censored density forecasts that quantify forecast risks in a middle region of the density covering a specified probability and ignore the magnitude but not the frequency of outlying observations. We propose a fixed‐point algorithm that fits a potentially skewed and fat‐tailed density to the inner observations, acknowledging that the outlying observations may…

函数型时间序列的自助法预测带AI 解读

Efstathios Paparoditisa and Han Lin ShangbaDepartment of Mathematics and Statistics, University of Cyprus, Nicosia, CyprusbDepartment of Actuarial Studies and Business Analytics, Macquarie Universi...

新闻驱动的不确定性波动AI 解读

We investigate the channels through which news influences the subjective beliefs of economic agents, with a particular focus on their subjective uncertainty. The main insight of the article is that news that is more at odds with agents’ prior beliefs generates an increase in uncertainty; news that is more consistent with their prior beliefs generates a decrease in uncertainty. We illustrate this insight theoretically and then estimate the model empirically using data on U.S.…

大型混合时变参数 VAR 模型AI 解读

Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few endogenous variables. Applying these models to high-dimensional datasets has proved to be challenging due to intensive computations and over-parameterization concerns. We develop an efficient Bayesian sparsification method for a class of models we call hybrid TVP-VARs—VARs with time-varying parameters in some equations but constant…

已实现协方差矩阵的奇异条件自回归 Wishart 模型AI 解读

Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in nonsingular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices may obtain as singular by…

通过合并 p 值检验多变量预测能力AI 解读

In this article, we propose an intersection-union test for multivariate forecast accuracy based on the combination of a sequence of univariate tests. The testing framework evaluates a global null hypothesis of equal predictive ability using any number of univariate forecast accuracy tests under arbitrary dependence structures, without specifying the underlying multivariate distribution. An extensive Monte Carlo simulation exercise shows that our proposed test has very good…

分组异质性中的结构突变AI 解读

Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Forecasting panel data under breaks offers the possibility to reduce parameter estimation error without inducing any bias if there exists a regime-specific pattern of grouped heterogeneity. To this end, we develop a new Bayesian methodology to estimate and formally test panel regression models in the presence of multiple breaks and unobserved…

利用经济新闻进行预测AI 解读

The goal of this article is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: (a) we consider only the text in the article that is semantically dependent on a term of interest (aspect-based) and, (b) assign a sentiment score to each word based on a dictionary that we develop for applications in economics and finance…

利用调查信息改进美国 GDP 的密度即时预测AI 解读

We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline…

非平稳时间序列的自回归近似、推断与应用AI 解读

Understanding the time-varying structure of complex temporal systems is one of the main challenges of modern time-series analysis. In this paper, we show that every uniformly-positive-definite-in-covariance and sufficiently short-range dependent nonstationary and nonlinear time series can be well approximated globally by a white-noise-driven autoregressive (AR) process of slowly diverging order. To our best knowledge, it is the first time such a structural approximation…