Abstract
Over the past 10 years dynamic stochastic general equilibrium (DSGE) models have become an important tool in quantitative macroeconomics. However, DSGE models were not considered as a forecasting tool until very recently. The objective of this paper is twofold. First, we compare the forecasting ability of a canonical DSGE model for the Spanish economy with other standard econometric techniques. More precisely, we compare out-of-sample forecasts obtained from different estimation methods of the DSGE model with the forecasts produced by a VAR and a Bayesian VAR. Second, we propose a new method for combining DSGE and VAR models (in what we have called Augmented VAR–DSGE) through the expansion of the variable space where the VAR operates with artificial series obtained from a DSGE model. The results indicate that the out-of-sample forecasting performance of the proposed method is capable of competing with all the considered alternatives, and thus even a simple canonical RBC model contains useful information that can be used for forecasting purposes.
Highlights
Forecasting macroeconomic variables is a crucial issue for both practitioners and policymakers, since the decisions of the former are based on the forecasts of key macroeconomic time series made by the latter
In this paper we propose a new approach to combining dynamic stochastic general equilibrium (DSGE) and VAR models
The proposed method is different from existing methods and consists of augmenting the space of the VAR with non-observables variables artificially generated by a DSGE model
Summary
Forecasting macroeconomic variables is a crucial issue for both practitioners and policymakers, since the decisions of the former are based on the forecasts of key macroeconomic time series made by the latter. We propose a new approach consisting of the expansion of the variables space where the VAR operates with the addition of artificial series obtained from a carefully calibrated dynamic general equilibrium model, as an alternative strategy of combining DSGE and VAR models. This new approach is simple, powerful and easy to implement empirically. The VAR can be augmented with unobserved variables and with observed ones, such as the stock of capital This approach can be interpreted as a new technique for mixing structural forecasting methods through DSGE models with standard nonstructural forecasting methods such as VAR and BVAR models.
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