Working Paper: CEPR ID: DP9379
Authors: Fabio Canova
Abstract: A method to estimate DSGE models using the raw data is proposed. The approach links the observables to the model counterparts via a flexible specification which does not require the model-based component to be solely located at business cycle frequencies, allows the non model-based component to take various time series patterns, and permits model misspecification. Applying standard data transformations induce biases in structural estimates and distortions in the policy conclusions. The proposed approach recovers important model-based features in selected experimental designs. Two widely discussed issues are used to illustrate its practical use.
Keywords: Business Cycles; DSGE Models; Filters; Structural Estimation
JEL Codes: C3; E3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| traditional filtering methods (C38) | biases in structural estimates (C51) |
| traditional filtering methods (C38) | distort policy conclusions (H31) |
| proposed method (C59) | more accurate estimates of structural parameters (C51) |
| proposed method (C59) | mitigates distortions caused by standard transformations (F12) |
| proposed methodology (C80) | better account for dynamics of output and inflation fluctuations (E39) |
| proposed methodology (C80) | clearer picture of underlying economic mechanisms (E65) |
| proposed methodology (C80) | improves reliability of policy implications (D78) |