Working Paper: NBER ID: w31709
Authors: Neville Francis; Michael Owyang; Daniel F. Soques
Abstract: We calculate impulse response functions from regime-switching models where the driving variable can respond to the shock. Two methods used to estimate the impulse responses in these models are generalized impulse response functions and local projections. Local projections depend on the observed switches in the data, while generalized impulse response functions rely on correctly specifying regime process. Using Monte Carlos with different misspecifications, we determine under what conditions either method is preferred. We then extend model-average impulse responses to this nonlinear environment and show that they generally perform better than either generalized impulse response functions and local projections. Finally, we apply these findings to the empirical estimation of regime-dependent fiscal multipliers and find multipliers less than one and generally small differences across different states of slack.
Keywords: No keywords provided
JEL Codes: C22; C24; E62
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| True model known (C59) | GIRFs provide accurate estimates of IRFs (C51) |
| Uncertainty about transition process (D89) | MAGIRFs outperform LP and single model (C52) |
| Lag order of VAR truncated (C32) | LP methods dominate GIRFs (F12) |
| True model known (C59) | GIRFs preferred (J16) |
| Uncertainty about transition process (D89) | MAGIRFs effectively capture causal dynamics (C69) |
| GIRFs provide reliable estimates (C51) | LP methods robust under conditions (C51) |