Working Paper: CEPR ID: DP6926
Authors: Daniel Ackerberg; Paul J. Devereux
Abstract: We introduce two simple new variants of the Jackknife Instrumental Variables (JIVE) estimator for overidentified linear models and show that they are superior to the existing JIVE estimator, significantly improving on its small sample bias properties. We also compare our new estimators to existing Nagar (1959) type estimators. We show that, in models with heteroskedasticity, our estimators have superior properties to both the Nagar estimator and the related B2SLS estimator suggested in Donald and Newey (2001). These theoretical results are verified in a set of Monte-Carlo experiments and then applied to estimating the returns to schooling using actual data.
Keywords: JIVE; Weak Instruments
JEL Codes: L24; L40; O31; O34
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| IJIVE and UIJIVE (C36) | small sample bias (C83) |
| number of exogenous variables (C51) | performance of JIVE estimator (C51) |
| heteroskedasticity (C21) | performance of IJIVE and UIJIVE (C67) |
| number of instruments (C36) | bias (D91) |
| IJIVE (C26) | bias term associated with number of exogenous variables (C51) |
| IJIVE and UIJIVE (C36) | outperform JIVE estimator (C51) |
| IJIVE and UIJIVE (C36) | superior properties under heteroskedasticity (C20) |