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利用混频数据改进密度预测：贝叶斯方法AI 解读

We construct a modeling framework that allows for the flexible contribution of intraday information to predict the conditional distribution of asset returns. A flexible spline specification is proposed to capture the contribution of intraday returns inside GARCH and stochastic volatility specifications for daily returns. The “focused Bayesian” prediction framework is adopted to allow the intraday contribution to the simpler GARCH specification to be trained according to the…

状态空间模型的高效重要性变分近似AI 解读

Variational methods are a potentially scalable estimation approach for state space models.However, existing methods are inaccurate or computationally infeasible for many state space models.This article proposes a variational approximation that is accurate and fast for any model with a closed-form measurement density function and a state transition distribution within the exponential family of distributions.Our approach constructs a variational approximation to the states…

高维潜变量模型的混合无校正 Langevin 方法AI 解读

The exact estimation of latent variable models with big data is known to be challenging. The latents have to be integrated out numerically, and the dimension of the latent variables increases with the sample size. This paper develops a novel approximate Bayesian method based on the Langevin diffusion process. The method employs the Fisher identity to integrate out the latent variables, which makes it accurate and computationally feasible when applied to big data. In contrast…

多项 Probit 模型的快速变分贝叶斯方法AI 解读

The multinomial probit model is often used to analyze choice behavior. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice datasets. This article proposes a variational Bayes method that is accurate and fast, even when a large number of choice alternatives and observations are considered. Variational methods usually require an analytical expression for the unnormalized posterior…

多项 Probit 模型中的可扩展贝叶斯估计AI 解读

The multinomial probit (MNP) model is a popular tool for analyzing choice behavior as it allows for correlation between choice alternatives. Because current model specifications employ a full covariance matrix of the latent utilities for the choice alternatives, they are not scalable to a large number of choice alternatives. This article proposes a factor structure on the covariance matrix, which makes the model scalable to large choice sets. The main challenge in estimating…

多潜变量模型的快速精确变分推断AI 解读

Models with a large number of latent variables are often used to utilize the information in big or complex data, but can be difficult to estimate. Variational inference methods provide an attractive solution. These methods use an approximation to the posterior density, yet for large latent variable models existing choices can be inaccurate or slow to calibrate. Here, we propose a family of tractable variational approximations that are more accurate and faster to calibrate…

采用异方差反演 Copula 的实时宏观经济预测AI 解读

There is a growing interest in allowing for asymmetry in the density forecasts of macroeconomic variables. In multivariate time series, this can be achieved with a copula model, where both serial and cross-sectional dependence is captured by a copula function, and the margins are nonparametric. Yet most existing copulas cannot capture heteroscedasticity well, which is a feature of many economic and financial time series. To do so, we propose a new copula created by the…