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Found 11 papers, 0 papers with code
Nowcasting with mixed frequency data using Gaussian processes
We propose and discuss Bayesian machine learning methods for mixed data sampling (MIDAS) regressions.
Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model
Timely information about the state of regional economies can be essential for planning, implementing and evaluating locally targeted economic policies.
Predictive Density Combination Using a Tree-Based Synthesis Function
Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods.
Bayesian Neural Networks for Macroeconomic Analysis
Neural networks, by contrast, are designed for datasets with millions of observations and covariates.
Bayesian Modeling of TVP-VARs Using Regression Trees
This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance.
Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty
We develop a non-parametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values.
General Bayesian time-varying parameter VARs for predicting government bond yields
Time-varying parameter (TVP) regressions commonly assume that time-variation in the coefficients is determined by a simple stochastic process such as a random walk.
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques
Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation.
Sparse time-varying parameter VECMs with an application to modeling electricity prices
In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroskedastic disturbances.
Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
Time-varying parameter (TVP) regression models can involve a huge number of coefficients.
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models
In this paper, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with KT explanatory variables.
