Working Paper: NBER ID: w22615
Authors: Francis X. Diebold; Frank Schorfheide; Minchul Shin
Abstract: Recent work has analyzed the forecasting performance of standard dynamic stochastic general equilibrium (DSGE) models, but little attention has been given to DSGE models that incorporate nonlinearities in exogenous driving processes. Against that background, we explore whether incorporating stochastic volatility improves DSGE forecasts (point, interval, and density). We examine real-time forecast accuracy for key macroeconomic variables including output growth, inflation, and the policy rate. We find that incorporating stochastic volatility in DSGE models of macroeconomic fundamentals markedly improves their density forecasts, just as incorporating stochastic volatility in models of financial asset returns improves their density forecasts.
Keywords: No keywords provided
JEL Codes: E17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Incorporating stochastic volatility (C58) | improves density forecasts for macroeconomic fundamentals (E37) |
| Incorporating stochastic volatility (C58) | better calibration of density forecasts (C53) |
| Incorporating stochastic volatility (C58) | improved predictive likelihood values (C52) |
| Incorporating stochastic volatility (C58) | improved coverage probabilities of interval forecasts (C53) |
| Incorporating stochastic volatility (C58) | more accurate forecasts for federal funds rate (E47) |
| Incorporating stochastic volatility (C58) | more accurate forecasts for inflation (E37) |
| Incorporating stochastic volatility (C58) | similar forecast accuracy for output growth (E17) |
| Stochastic volatility models (C58) | superior forecast accuracy (C53) |