Robust, accurate confidence intervals with a weak instrument: quarter of birth and education

By Guido W. Imbens, Paul R. Rosenbaum. 2005

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, background): “Another approach, not further discussed here, is the randomization-based approach to instrumental variables developed in Imbens and Rosenbaum (2005).”
The minimum wage and the Great Recession: Evidence of effects on the employment and income trajectories of low-skilled workers (2014, methodology): “The first applies the classic approach as described by Imbens and Rosenbaum (2005), in which we assign treatment at random across the 51 states as they appear in our baseline analysis sample.”
Finite Population Causal Standard Errors (2014, methodology): “For applications of randomization inference in observational studies see Rosenbaum (2002), Abadie, Diamond and Hainmueller (2010), Imbens and Rosenbaum (2005), Frandsen (2012), Bertrand, Duflo, and Mullainathan (2004) and Barrios, Diamond, Imbens and Kolesar (2012).”
Estimating peer effects in networks with peer encouragement designs (2016, methodology): “2 In this setting, for the finite population Pegos, exact inference for γ is possible (17) by inverting a hypothesis test for γ = γ0.”
War and Wages (2008, methodology): “Write Dβ0i for the matched pair difference in Rij − β0Wij , encouraged (Zij = 1) minus control (Zij = 0), so that D β0 i = Zi1{(Ri1 − β0Wi1) − (Ri2 − β0Wi2)} + (1 − Zi1){(Ri2 − β0Wi2) − (Ri1 − β0Wi1)} = (β − β0){Zi1(wT i1 − wCi2) + (1 − Zi1)(wT i2 − wCi1)} + (2Zi1 − 1)(ai1 − ai2) = (β − β0)Si + i, (”
A review of instrumental variable estimators for Mendelian randomization (2015, methodology): “The empirical distribution of the IV estimator in the bootstrapped samples approximates the true distribution of the IV estimator.(44) However, there are some concerns about the behaviour of bootstrapped confidence intervals with weak instruments.”
Shock-Based Causal Inference in Corporate Finance and Accounting Research (2015, background): “86Imbens and Rosenbaum (2005) propose the use of randomization inference to develop confidence intervals for a weak instrument.”
Randomization Inference in a Group–Randomized Trial of Treatments for Depression (2008, methodology): “5; Greevy, Silber, Cnaan, and Rosenbaum 2004; Imbens and Rosenbaum 2005); however, we use the group randomization (1.1) together with patient level compliance.”
Instrumental Variables: An Econometrician's Perspective (2014, unknown): “A very different approach is taken in Rosenbaum (1996) and Imbens and Rosenbaum (2005).”
Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate (2015, methodology): “Our randomization inference framework, however, circumvents this concern because it enables us to conduct exact finite-sample inference, as discussed in Imbens and Rosenbaum [10] for the usual IV setting.”
The WTO trade effect (2011, methodology): “Recently, Imbens and Rosenbaum (2005) applied permutation inference to well-known “weak instrument” data in economics to find that only permutation methods provided reliable inference.”
Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology (2008, unknown): “However, in recent years, econometricians have realized that with very weak instruments the usual asymptotic methods of IV estimation perform badly, giving biased estimates and confidence intervals that are too narrow, even in samples that would normally be thought large enough for asymptotic result”
Powerful Women: Does Exposure Reduce Bias? (2008, background): “Questions included “Do you think the Pradhan has done a good job looking after the needs of your village?” and “How would you rank the effectiveness of the current Pradhan?””
Randomization Inference With Natural Experiments (2006, methodology): “Finally , the confidence interval based on randomization inference will be unidentified when the data are not informative (e.g., Imbens and Rosenbaum 2005).”