Discontinuities of weak instrument limiting distributions

By Jinyong Hahn, G. Kuersteiner. 2002

69 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/530078a609b21d31d7f0586c2cd74fb8c17d4b1a

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In Statistics and Probability

Citations in the corpus

Weak Identification in Fuzzy Regression Discontinuity Designs (2016, background): “This situation resembles so-called nearly weak or semistrong identification, see Hahn and Kuersteiner (2002), Caner (2009), Antoine and Renault (2009, 2012), and Antoine and Lavergne (2014).”
Selecting Instrumental Variables in a Data Rich Environment (2009, methodology): “We call those Qjt ⊂ Qt with small explanatory power for x2t the ‘relatively weak’ instruments, defined in Hahn and Kuersteiner (2002) as instruments with Ψj in the T−δ neighborhood of zero with δ 12 .”
Efficient Minimum Distance Estimation with Multiple Rates of Convergence (2012, methodology): “Example 2 (Nearly-weak instruments) For GMM with nearly-weak instruments, as introduced by Caner (2008) for a non-linear extension of Hahn and Kuersteiner (2002), the correlation between the instruments and the first-order conditions declines at a rate slower than root-T .”
The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models (2010, unknown): “The approximate bias of the 2SLS estimator can be obtained using higher order asymptotics based on the expansion in (14), see Nagar (1959) , Buse (1992) and Hahn and Kuersteiner (2002).”
INSTRUMENTAL VARIABLE ESTIMATION IN A DATA RICH ENVIRONMENT (2010, methodology): “We use data collected in Ludvigson and Ng (2005), which consist of quarterly observations on a panel of 209 quarterly macroeconomic series for the sample 1960:1-2002:4, where some are monthly series aggregated to quarterly levels.”