Symmetric Jackknife Instrumental Variable Estimation
AbstractThis paper gives a new jackknife estimator for instrumental variable inference with unknown heteroskedasticity. The estimator is derived by using a method of moments approach similar to the one that produces LIML in case of homoskedasticity. The estimator is symmetric in the endogenous variables including the dependent variable. Many instruments and many weak instruments asymptotic distributions are derived using high-level assumptions that allow for the simultaneous presence of weak and strong instruments for different explanatory variables. Standard errors are formulated compactly. We review briefly known estimators and show in particular that the symmetric jackknife estimator performs well when compared to the HLIM and HFUL estimators of Hausman et al. (2011) in Monte Carlo experiments.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 37853.
Date of creation: 05 Apr 2012
Date of revision:
Instrumental Variables; Heteroskedasticity; many Instruments; Jackknife;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
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