The asymptotic variance of semiparametric estimators

By Whitney K. Newey. 1994

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers Victor Chernozhukov†, Whitney K. Newey†, and Rahul Singh† †MIT Economics, 50 Memorial Drive, Cambridge MA 02142, USA.”
Generalized random forests (2016, methodology): “It is the same quantity that is needed to estimate variance of classical local maximum likelihood methods following Newey (1994a); e.g., for the instrumental variables problem described in Section 7, (17) V (x) = ( E [ ZiWi ∣∣Xi = x] E [Zi ∣∣Xi = x] E [ Wi ∣∣Xi = x] 1 ) , while for quantile…”
Learning When-to-Treat Policies (2019, methodology): “We propose an advantage doubly robust estimator that draws upon the semiparametrics and orthogonal moments literature (Belloni et al., 2014; Chernozhukov et al., 2016a; Newey, 1994; Robins and Rotnitzky, 1995; Scharfstein et al., 1999).”
Double/Debiased Machine Learning for Treatment and Causal Parameters (2016, methodology): “(Concentrating-out Approach) The approach for constructing Neyman orthogonal scores described above is closely related to the following concentrating-out approach which has been used, for example, in Newey (1994), to show Neyman orthogonality when is innite dimensional. For all 2, let be the solutio”
Efficient Policy Learning (2017, methodology): “This estimator is closely related to the classical semiparametric two-stage methods studied by, e.g., Hahn (1998), Newey (1994) and Robins and Rotnitzky (1995).”
Quasi-oracle estimation of heterogeneous treatment effects (2017, background): “…eﬃciency and constructions of orthogonal moments including Robinson (1988) and, more broadly, Belloni et al. (2017), Bickel et al. (1998), Newey (1994), Robins (2004), Robins and Rotnitzky (1995), Robins et al. (2017), Tsiatis (2007), van der Laan and Rose (2011), etc., that aim at √ n…”
The influence function of semiparametric estimators (2015, methodology): “The influence function φ(w, γ0, α0, θ) of E[g(W , γ(F ), θ)] is an “adjustment term,” or first- step influence function (FSIF) analyzed in Newey (1994), that accounts for the presence of the first step γ̂ in the moment functions.”
Local indirect least squares and average marginal effects in nonseparable structural systems (2012, background): “Whereas Newey (1994) provides correction terms for conditional expectations and densities (and their derivatives), we give correction terms for gV; (z).”
Identification and Estimation of Regression Models with Misclassification (2006, unknown): “The first method is a minimum distance estimator along the lines of Example 2 Newey (1994a) and the second is a sieve maximum likelihood estimator which in practice reeduces to a parametric estimation procedure and is therefore relatively straightforward to implement.”
Fuzzy Differences-in-Differences (2015, methodology): “ (X ) 1; m U J =1 (X ) . Then Condition 6.6-(ii) holds by applying the same reasoning as in Frölich (2007), pp.68-69, and because both g0 and are s times dierentiable. Hence, Conditions 6.1-6.6 of Newey (1994) hold. By the proof of his Theorem 6.1, this implies that his Conditions 5.1-5.3 also hold”
Characterization of the asymptotic distribution of semiparametric M-estimators (2006, background): “ponents do not aect the asymptotic distribution. Andrews (1994a) and Newey (1994) have”
Sensitivity to Missing Data Assumptions: Theory and an Evaluation of the U.S. Wage Structure (2010, background): “1, it is, in principle, possible to employ nonparametric estimators for qL(τ k|·) and qU(τ k|·), and to exploit that πL(τ k) and πU(τ k) are smooth functionals to obtain asymptotically normal estimators pointwise in (τ k) without a discrete support requirement on X (Newey (1994) and Chen, Linton, an”
Identification and Estimation of Distributional Impacts of Interventions Using Changes in Inequality Measures (2016, unknown): “(T; p (X)) 1IfY yg] : Proposition 1 follows after we specialize the results in Newey (1994) to the missing data case and Hirano, Imbens and Ridder (2003) and Chen, Hong and Tarozzi (2008) to the indicator function.”
Semiparametric estimation of a simultaneous game with incomplete information (2010, unknown): “n=1 ∂(2)`Z ( wn, θ̃, π̂N (θ̃, zn) ) ∂θ∂θ ′ p −→ E [ ∂(2)`Z ( W,θ0,π (θ0,Z) ) ∂θ∂θ ′ ] = −=Z (6)”
Binary choice panel data models with predetermined variables (2003, methodology): “Notice that this is also true of the static random eeects probit model of Chamberlain (1984) and Newey (1994a).”
Efficient estimation of average treatment effects using the estimated propensity score (2003, unknown): “and this eigenvalue is bounded from 0 with probability 1, if kŜK(N) − SKk p → 0 (Newey (1995), Lemma A.4) which is the case if ζ(K(N)) 4 N → 0 (Newey (1995), Lemma A.”
An Estimation of Economic Models with Recursive (2007, unknown): “…(19) is misspecified if min δ∈D inf F∈V E [ m(wt δ F)′m(wt δ F) ] > 0 We estimate the possibly misspecified model (19) using a profile semiparametric minimum distance procedure, which consists of two steps; see, for example, Newey (1994), Chen, Linton, and van Keilegom (2003), and Chen (2007).”