Bayesian Nonparametric Graphical Models for Time-Varying Parameters VAR
نویسندگان
چکیده
منابع مشابه
Bayesian Bandwidth Selection in Nonparametric Time - Varying Coefficient Models
Bandwidth plays an important role in determining the performance of local linear estimators. In this paper, we propose a Bayesian approach to bandwidth selection for local linear estimation of time–varying coefficient time series models, where the errors are assumed to follow the Gaussian kernel error density. A Markov chain Monte Carlo algorithm is presented to simultaneously estimate the band...
متن کاملBayesian Bandwidth Estimation in Nonparametric Time-Varying Coefficient Models
Bandwidth plays an important role in determining the performance of nonparametric estimators, such as the local constant estimator. In this paper, we propose a Bayesian approach to bandwidth estimation for local constant estimators of time–varying coefficients in time series models. We establish a large sample theory for the proposed bandwidth estimator and Bayesian estimators of the unknown pa...
متن کاملCorrected portmanteau tests for VAR models with time-varying variance
The problem of test of fit for Vector AutoRegressive (VAR) processes with unconditionally heteroscedastic errors is studied. The volatility structure is deterministic but time-varying and allows for changes that are commonly observed in economic or financial multivariate series such as breaks or smooth transitions. Our analysis is based on the residual autocovariances and autocorrelations obtai...
متن کاملSparse Nonparametric Graphical Models
We present some nonparametric methods for graphical modeling. In the discrete case, where the data are binary or drawn from a finite alphabet, Markov random fields are already essentially nonparametric, since the cliques can take only a finite number of values. Continuous data are different. The Gaussian graphical model is the standard parametric model for continuous data, but it makes distribu...
متن کاملNonparametric Bayesian Models for Neural
of “Nonparametric Bayesian Models for Neural Data” by Frank Wood, Ph.D., Brown University, May 2007. Many neural data analyses can be cast as latent variable modeling problems. Specific examples include spike sorting and neurological data analysis. Challenges in spike sorting include figuring out how many neurons generated a set of recorded action potentials and, further, which neuron generated...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2019
ISSN: 1556-5068
DOI: 10.2139/ssrn.3400078