Working Paper: NBER ID: w14872
Authors: Frank Schorfheide; Keith Sill; Maxym Kryshko
Abstract: This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to the state variables of the DSGE model. Predictions for the non-core variables are obtained by applying their measurement equations to DSGE model-generated forecasts of the state variables. Using a medium-scale New Keynesian DSGE model, we apply our approach to generate and evaluate recursive forecasts for PCE inflation, core PCE inflation, the unemployment rate, and housing starts along with predictions for the seven variables that have been used to estimate the DSGE model.
Keywords: DSGE Models; Forecasting; Noncore Variables; Monetary Policy
JEL Codes: C11; C32; C53; E27; E47
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Monetary policy shocks (E39) | Noncore variables (C29) |
| Latent state variables (C32) | Noncore variables (C29) |
| Monetary policy shocks (E39) | Core PCE inflation (E31) |
| Monetary policy shocks (E39) | Unemployment rate (J64) |
| Monetary policy shocks (E39) | Housing starts (R31) |
| Latent state variables (C32) | Core PCE inflation (E31) |
| Latent state variables (C32) | Unemployment rate (J64) |
| Latent state variables (C32) | Housing starts (R31) |