Bayesian estimation of large dimensional time varying VARs using copulas
This paper provides a simple, yet reliable, alternative to the (Bayesian) estimation of large multivariate VARs with time variation in the conditional mean equations and/or in the covariance structure. With our new methodology, the original multivariate, n dimensional model is treated as a set of n univariate estimation problems, and cross-dependence is handled through the use of a copula. Thus, only univariate distribution functions are needed when estimating the individual equations, which are often available in closed form, and easy to handle with MCMC (or other techniques). Estimation is carried out in parallel for the individual equations. Thereafter, the individual posteriors are combined with the copula, so obtaining a joint posterior which can be easily resampled. We illustrate our approach by applying it to a large time-varying parameter VAR with 25 macroeconomic variables.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
High-dimensional macroeconomic forecasting using message passing algorithms
This paper 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…
regressionVocal Bursts Intensity PredictionImplicit Maximum a Posteriori Filtering via Adaptive Optimization
Bayesian filtering approximates the true underlying behavior of a time-varying system by inverting an explicit generative model to convert noisy measurements into state estimates. This process typically requires either s…
Distributed Time-Varying Gaussian Regression via Kalman Filtering
We consider the problem of learning time-varying functions in a distributed fashion, where agents collect local information to collaboratively achieve a shared estimate. This task is particularly relevant in control appl…
regressionAnalytic solution of the exact Daum-Huang flow equation for particle filters
State estimation for nonlinear systems, especially in high dimensions, is a generally intractable problem, despite the ever-increasing computing power. Efficient algorithms usually apply a finite-dimensional model for ap…
State EstimationHigh-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model
The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. However, these tissue properties are spatially varying across the underlying anatom…
AnatomyBayesian Optimizationmodelparameter estimation