Semiparametric Efficiency Bound for Models of Sequential Moment Restrictions Containing Unknown Functions

By Chunrong Ai, Xiaohong Chen. 2009

74 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0641137b42573e9859e92a5e084071591000403b

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Citations in the corpus

Identification Properties of Recent Production Function Estimators (2015, methodology): “While these moment conditions could be estimated jointly as in Ai and Chen or Wooldridge (2009), estimation using a two-stage “plug-in” approach is consistent (see Ai and Chen (2007)) and popular in the empirical literature for computational reasons.”
Double/debiased machine learning for treatment and structural parameters (2017, unknown): “See, for example, the work by Levit (1975), Ibragimov and Hasminskii (1981), Bickel (1982), Robinson (1988), Newey (1990), van der Vaart (1991), Andrews (1994a), Newey (1994), Newey et al. (1998), Robins and Rotnitzky (1995), Linton (1996), Bickel et al. (1998), Chen et al. (2003), Newey et al.”
Locally Robust Semiparametric Estimation (2016, background): “Bang and Robins (2005). Van der Laan and Rubin (2006) developed targeted maximum likelihood to obtain a LR estimating equation based on the efficient influence function of a semiparametric model. Robins et al. (2008, 2017) showed that efficient influence functions are LR, characterized some doubly r”
The influence function of semiparametric estimators (2015, background): “Newey (1994) gave general explicit influence function formulae where a first step is an infinite dimensional regressions or pdf. Ai and Chen (2007, 2012), Ichimura and Lee (2010), Ackerberg et al. (2014), Chen and Liao (2015), and Chen and Pouzo (2015) gave interesting and useful characterizations…”
Double machine learning for treatment and causal parameters (2016, unknown): “See, for example, the important work by [14], [50], [40], [54], [2], [41], [49], [38], [15], [19], [53], and [1].”
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, methodology): “In this section we brieﬂy review how one approach to this, following the application of the method of Ai and Chen (2012) to this set of conditional moment equation, can obtain the necessary fourth-root rates for use in DRL.”
Applied Nonparametric Instrumental Variables Estimation (2011, methodology): “Ai and Chen (2009) provide asymptotic distributional results for nonparametric IV estimators of unconditional average derivatives, but there is no existing theory on nonparametric IV estimation of conditional average derivatives such as ( , | 1.”
Sieve Wald and QLR Inferences on Semi/Nonparametric Conditional Moment Models (2014, unknown): “These models are often ill-posed and hence it is difficult to verify whether a (possibly nonlinear) functional is root-n estimable or not.”