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Found 8 papers, 0 papers with code
High-Dimensional Conditionally Gaussian State Space Models with Missing Data
A key insight underlying the proposed approach is that the joint distribution of the missing data conditional on the observed data is Gaussian.
Comparing Stochastic Volatility Specifications for Large Bayesian VARs
Large Bayesian vector autoregressions with various forms of stochastic volatility have become increasingly popular in empirical macroeconomics.
Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility
We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs.
Large Hybrid Time-Varying Parameter VARs
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 coefficients in others.
Efficient Estimation of State-Space Mixed-Frequency VARs: A Precision-Based Approach
Results from these two empirical applications highlight the importance of incorporating high-frequency indicators in macroeconomic models.
Large Order-Invariant Bayesian VARs with Stochastic Volatility
Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic volatility are not invariant to the way the variables are ordered due to the use of a Cholesky decomposition for the error covariance matrix.
Asymmetric Conjugate Priors for Large Bayesian VARs
One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results.
On Parameter Estimation in Unobserved Components Models subject to Linear Inequality Constraints
We propose a new \textit{quadratic programming-based} method of approximating a nonstandard density using a multivariate Gaussian density.
