Working Paper: CEPR ID: DP7542
Authors: Antonello Dagostino; Luca Gambetti; Domenico Giannone
Abstract: The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coefficients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the naïve random walk model. These results are also shown to hold over the most recent period in which it has been hard to forecast inflation.
Keywords: forecasting; inflation; stochastic volatility; time varying vector autoregression
JEL Codes: C32; E37; E47
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| structural changes (L16) | forecasting accuracy (C53) |
| TVVAR model (C32) | forecasting accuracy for inflation (E37) |
| TVVAR model (C32) | forecasting accuracy for unemployment (E27) |
| TVVAR model (C32) | forecasting accuracy for interest rates (E47) |
| TVVAR model (C32) | lower mean square forecast errors (MSFE) for inflation (E17) |