THE CASE AGAINST JIVE

By Russell Davidson, James G. MacKinnon. 2006

67 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/162e7b9ed5462af423b4ca5861adb15cf0118c71

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Citations in the corpus

Estimation with Weak Instruments: Accuracy of Higher-order Bias and MSE Approximations (2004, unknown): “Hausman (1983) for a derivation and analysis.”
Jackknife estimation of stationary autoregressive models (2013, unknown): “…and demonstrated its superior finite sample properties compared to the two-stage least squares estimator and its comparability to the limited information maximum likelihood estimator, although the performance of this estimator has subsequently been criticised by Davidson and MacKinnon (2006).”
Estimation With Many Instrumental Variables (2006, result): “These results are also consistent with recent Monte Carlo work of Davidson and MacKinnon (2006).”
ASYMPTOTIC DISTRIBUTION OF JIVE IN A HETEROSKEDASTIC IV REGRESSION WITH MANY INSTRUMENTS (2011, background): “We show that JIV estimators are asymptotically normal and that standard errors are consistent provided that√ Kn/rn → 0 as n → ∞, where Kn and rn denote, respectively, the number of instruments and the concentration parameter.”
Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity (2009, unknown): “X CI J I V E = X (Pz − DPz)(I − DPz)−1 (13) = (Zπ + η) (Pz − DPz)(I − DPz)−1 = π Z (I − DPz)(I − DPz)−1 + η CI J I V E = π Z + η CI J I V E .”
Controlling for endogeneity with instrumental variables in strategic management research (2008, methodology): “For the OLS estimation, the impact of dividends on excess value is positive and highly significant; for the fixed-effects estimation, the impact is negative and significant at the 10 percent level; and for the FGLS estimation, the impact is almost 0 and not significant.35 The three instrumental vari”
Instrumental Variable Estimation with Heteroskedasticity and Many Instruments (2012, methodology): “It is also as efficient asymptotically and performs as well in our Monte Carlo results as LIML under homoskedasticity, thus overcoming the Davidson and MacKinnon (2006) objection to JIV.”