Working Paper: CEPR ID: DP10382
Authors: Vo Phuong Mai Le; David Meenagh; Patrick Minford; Michael Wickens
Abstract: Using Monte Carlo experiments, we examine the performance of indirect inference tests of DSGE models in small samples, using various models in widespread use. We compare these with tests based on direct inference (using the Likelihood Ratio). We find that both tests have power so that a substantially false model will tend to be rejected by both; but that the power of the indirect inference test is by far the greater, necessitating re-estimation to ensure that the model is tested in its fullest sense. We also find that the small-sample bias with indirect estimation is around half of that with maximum likelihood estimation.
Keywords: Bootstrap; DSGE; Indirect Inference; Likelihood Ratio; New Classical; New Keynesian; Wald Statistic
JEL Codes: C12; C32; C52; E1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Small sample bias with indirect estimation (C83) | approximately half that of maximum likelihood estimation (C51) |
| Likelihood ratio test (C52) | weak power relative to indirect inference (C51) |
| Indirect inference (C36) | greater power than likelihood ratio test (C52) |