We compare the forecasting performances of the classical and the Minnesota-type Bayesian vector autoregressive (VAR … algorithm for variable selection, estimated using Markov Chain Monte Carlo methods. In this regard, we analyze the forecasting … the linear fixed coefficients classical VAR. However, we do not observe marked gains in forecasting power across the …

This paper evaluates the ability of Bayesian shrinkage-based dynamic predictive regression models estimated with … (Gaussian, Lasso-LARS, Lasso-Landweber) in forecasting the U.S. real house price growth. We also compare results with forecasts …) MSE-F statistic, indicate that in general, the non-hierarchical Bayesian shrinkage estimators perform better than their …

This paper evaluates the performance of 11 vector autoregressive models in forecasting 15 macroeconomic variables for …

-scale models are better-suited for forecasting the three macroeconomic variables of our choice, and amongst the two types of large …

accommodates a large cross-section of macroeconomic time series for forecasting per capita growth rate, inflation, and the nominal …, Bayesian VARs (BVARs) and a typical New Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model in forecasting the …

time series for forecasting regional house price inflation. As a case study, we use data on house price inflation for five …

prove to be more useful in forecasting real house price growth rate of the nine census divisions of the US, compared to the …

forecasting annualized real house price growth rates for large-, medium- and small-middle-segment housing for the South African …

double-exponential prior in forecasting the real house price of the United States (US), based on a monthly dataset of 112 …-suited for forecasting the real US house price. Amongst the Bayesian models, the regression based on the double exponential prior …