Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions
By Chunrong Ai, Xiaohong Chen. 2003
859 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d5fa6b2cd43fc175696e8476001942fcad5afd5c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Victor Chernozhukov
- Whitney K. Newey
- Xiaohong Chen
- Stefan Wager
- Guido W. Imbens
- Arthur Lewbel
- Christian Hansen
- Susan Athey
Citations in the corpus
- Testing the quantity–quality model of fertility: Estimation using unrestricted family size models (2016, methodology): “It is important, however, to emphasize that IV estimators using these efficient instruments will be robust to misspecification of the functional form (see, e.g., Newey (1990, 1993)).”
- Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, methodology): “Ai and Chen (2003) prove that applying the above with appropriate nonparametric estimators, under some smoothness conditions, we can obtain ˆ q t − q t F,t = O p ( n − 1 / 4 ), where · F,t is the Fisher metric, which is defined as We omit the details and refer the interested reader to Ai and Chen…”
- Program evaluation with high-dimensional data (2013, background): “Our work also contributes to the line of research on obtaining √ n-consistent and asymptotically normal estimates for low-dimensional components within traditional semiparametric frameworks, specifically in the important work by Bickel (1982), Robinson (1988), Newey (1990), van der Vaart (1991), And”
- Consistent Estimation of Models Defined by Conditional Moment Restrictions (2004, methodology): “Denote the gradient and Hessian of Qn(θ) as Q̇n(θ) = ∂Qn(θ) ∂θ and Q̈n(θ) = ∂ 2Qn(θ) ∂θ∂θ′ Note that in the general case, both Q̇n(θ) and Q̈n involve estimating some conditional expectations; see Newey (1993).”
- Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models (2012, methodology): “Examples include Ai and Chen (2003), Otsu (2003), and Newey and Powell (2003).”
- Local Linear GMM Estimation of Functional Coefficient IV Models With an Application to Estimating the Rate of Return to Schooling (2013, methodology): “We do so by extending the work of Newey (1990, 1993), Baltagi and Li (2002), and Ai and Chen (2003) to our framework, but the latter authors only consider OIVs for GMM estimates of finite dimensional parameters based on conditional moment conditions.”
- The influence function of semiparametric estimators (2015, methodology): “This γ̂ has the form of NPIV given in Newey and Powell (1989, 2003), Newey (1991), Ai and Chen (2003), and Darolles, Fan, Florens, and Renault (2011).”
- Semiparametric GMM estimation of spatial autoregressive models (2012, methodology): “In addition, the asymptotic distribution of the estimator of the nonparametric component is also of our main interest, which is unfortunately not addressed in Ai and Chen (2003).”
- ON THE ASYMPTOTIC EFFICIENCY OF GMM (2013, methodology): “Donald and Newey (2001) give a method for selecting the optimal number of instruments by minimizing the mean square error of the estimator. One difference between projection and the other three regularizations mentioned earlier is that the projection requires an a priori ordering of the instruments ”
- Estimation of Semiparametric Models When the Criterion Function is Not Smooth (2002, positive): “, Bickel, Klaassen, Ritov, and Wellner (1993), Andrews (1994a), Newey (1994), Newey and McFadden (1994, Section 8), Pakes and Olley (1995), Chen and Shen (1998) and Ai and Chen (1999)].”
- High dimensional stochastic regression with latent factors, endogeneity and nonlinearity (2013, background): “See Ai and Chen (2003) for further detail on the sieve method.”
- Semiparametric estimation of a simultaneous game with incomplete information (2010, methodology): “Following the approach of Newey (1990), efficiency bounds for models with conditional moment restrictions can be found in Ai and Chen (2003).”
- NONPARAMETRIC IDENTIFICATION USING INSTRUMENTAL VARIABLES: SUFFICIENT CONDITIONS FOR COMPLETENESS (2011, methodology): “These conditions can be used to obtain global or local identification in a variety of models including the nonparametric IV regression model (see Newey and Powell (2003); Darolles, Fan, Florens, and Renault (2011); Hall and Horowitz (2005); Horowitz (2011)), semiparametric IV models (see Ai and Chen”