Meenagh, David ORCID: https://orcid.org/0000-0002-9930-7947, Minford, Anthony ORCID: https://orcid.org/0000-0003-2499-935X and Xu, Yongdeng ORCID: https://orcid.org/0000-0001-8275-1585 2023. Indirect inference and small sample bias — Some recent results. Open Economies Review 10.1007/s11079-023-09731-8 |
PDF
- Published Version
Available under License Creative Commons Attribution. Download (1MB) |
Abstract
Macroeconomic researchers use a variety of estimators to parameterise their models empirically. One such is FIML; another is indirect inference (II). One form of indirect inference is ‘informal’ whereby data features are ‘targeted’ by the model — i.e. parameters are chosen so that model-simulated features replicate the data features closely. Monte Carlo experiments show that in the small samples prevalent in macro data, both FIML informal II produce high bias, while formal II, in which the joint probability of the data- generated auxiliary model is maximised under the model simulated distribution, produces low bias. They also show that FII gets this low bias from its high power in rejecting misspecified models, which comes in turn from the fact that this distribution is restricted by the model-specified parameters, so sharply distinguishing it from rival misspecified models.
Item Type: | Article |
---|---|
Date Type: | Published Online |
Status: | In Press |
Schools: | Business (Including Economics) |
Subjects: | H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory |
Publisher: | Springer |
ISSN: | 0923-7992 |
Date of First Compliant Deposit: | 29 September 2023 |
Date of Acceptance: | 31 July 2023 |
Last Modified: | 30 Sep 2023 02:06 |
URI: | https://orca.cardiff.ac.uk/id/eprint/162171 |
Actions (repository staff only)
Edit Item |