This paper evaluates the performance of 11 vector autoregressive models in forecasting 15 macroeconomic variables for the Indian economy over the 2007:01 to 2011:10 out-of-sample period. We consider 3 classical VARs, 4 Bayesian VARs and 4 Bayesian Factor Augmented VARs. Comparing the performance...

This paper uses a predictive regression framework to examine the out-of-sample predictability of South Africa’s equity premium, using a host of financial and macroeconomic variables. Past studies tend to suggest that the predictors on their own fail to deliver consistent out-of-sample forecast...

This paper uses the Dynamic Factor Model (DFM) framework, which accommodates a large cross-section of macroeconomic time series for forecasting regional house price inflation. As a case study, we use data on house price inflation for five metropolitan areas of South Africa. The DFM used in this...

This paper uses large Factor Models (FMs) which accommodates a large cross-section of macroeconomic time series for forecasting per capita growth rate, inflation, and the nominal short-term interest rate for the South African economy. The FMs used in this study contains 267 quarterly series...

This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01 to...

This paper uses two-types of large-scale models, namely the Dynamic Factor Model (DFM) and Bayesian Vector Autoregressive (BVAR) Models based on alternative hyperparameters specifying the prior, which accommodates 267 macroeconomic time series, to forecast key macroeconomic variables of a small...

This paper uses Dynamic Factor Models (DFMs), estimated under both classical and Bayesian assumptions, which accommodates a large cross-section of macroeconomic time series for forecasting per capita growth rate, inflation, and the nominal short-term interest rate for the South African economy....

This paper uses the Dynamic Factor Model (DFM) framework, which accommodates a large cross-section of macroeconomic time series for forecasting regional house price inflation. As a case study, we use data on house price inflation for five metropolitan areas of South Africa. The DFM used in this...

This paper analyzes whether a wealth of information contained in 126 monthly series used by large-scale Bayesian Vector Autoregressive (LBVAR) models, as well as Factor Augmented Vector Autoregressive (FAVAR) models, either Bayesian or classical, can prove to be more useful in forecasting real...

This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium- and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01 to...