Since the seminal article of Bates and Granger (1969), a large number of theoretical and empirical studies have shown that pooling different forecasts of the same event tends to outperform individual forecasts in terms of forecast accuracy. However, the results remain heterogenous regarding the...

Since the seminal article of Bates and Granger (1969), a large number of theoretical and empirical studies have shown that pooling different forecasts of the same event tends to outperform individual forecasts in terms of forecast accuracy. However, the results remain heterogenous regarding the...

Dynamic stochastic general equilibrium models have recently become standard tools for policy-oriented analyses. Nevertheless, their forecasting properties are still barely explored. We fill this gap by comparing the quality of real-time forecasts from a richly-specified DSGE model to those from...

This paper analyses the international linkages of the Korean economy using the GVAR model developed by Greenwood-Nimmo, Nguyen and Shin (2012a, J. Appl. Econometrics). By employing a combination of generalised impulse response analysis and forecast error variance decompositions, we uncover a...

This chapter demonstrates the usefulness of the GVAR modelling framework as a tool for scenario-based forecasting and counterfactual analysis. Working with the GVAR model developed by Greenwood-Nimmo, Nguyen and Shin (2010, J. Appl. Econometrics), we first show how probabilistic forecasting can...

Dynamic stochastic general equilibrium models have recently become standard tools for policy-oriented analyses. Nevertheless, their forecasting properties are still barely explored. We fill this gap by comparing the quality of real-time forecasts from a richly-specified DSGE model to those from...

We introduce a structural quantile vector autoregressive (VAR) model. Unlike standard VAR which models only the average interaction of the endogenous variables, quantile VAR models their interaction at any quantile. We show how to estimate and forecast multivariate quantiles within a recursive...

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require...

We introduce a structural quantile vector autoregressive (VAR) model. Unlike standard VAR which models only the average interaction of the endogenous variables, quantile VAR models their interaction at any quantile. We show how to estimate and forecast multivariate quantiles within a recursive...