Kernel Estimation of Partial Means and a General Variance Estimator

By Whitney K. Newey. 1994

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Citations in the corpus

Local Linear Forests (2018, methodology): “…helpful to use a local regression adjustment to correct for potential misalignment between a test point and its neighborhood (Stone 1977; Tibshirani and Hastie 1987; Cleveland and Devlin 1988; Newey 1994; Fan and Gijbels 1996; Heckman, Ichimura, and Todd 1998; Loader 1999; Abadie and Imbens 2011).”
Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “In lowdimensional nonparametrics, the study of such functionals, called “partial means” goes back, e.g., to Newey (1994b).”
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…”
Smooth backfitting in practice (2005, background): “E-mail: stefan@est-econ.uc3m.es (Newey, 1994; Tjøstheim and Auestad, 1994; Linton and Nielsen, 1995) was more promising and attracted considerable attention; see Sperlich (1998).”
Estimating Semiparametric Panel Data Models by Marginal Integration (2012, methodology): “This method is independently invented by Newey (1994) and Linton and Nielsen (1995) in the context of i.i.d. cross-section regression.”
Series Estimation of Partially Linear Panel Data Models with Fixed Effects (2002, methodology): “Recently semiparametric estimation of additive models and additive partially linear models have received much attention, see Linton and Nielsen (1995), Newey (1994), Fan et al. (1998) and Li (2000) to mention a few.”
Complementarity and Aggregate Implications of Assortative Matching: A Nonparametric Analysis (2014, unknown): “The usual Nadaraya–Watson kernel regression estimator has a uniform rate of convergence on the internal region of support of the conditioning variable (e.g., Newey (1994)).”
Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints (2012, methodology): “Fixed Trimming: To avoid some technical issues related to boundary bias, we use the following "fixed trimming" modification, as in Newey (1994), theorem 4.1.”
Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors (2005, methodology): “The modification incorporates random trimming instead of the fixed trimming considered in Newey (1994).”
Testing conditional moment restrictions (2003, methodology): “Since Lemmas B.1–B.3 of Newey (1994) remain valid when θ0 is replaced by θn,0 [because g(z1, θn,0), . . . , g(zn, θn,0) are i.i.d. for each n], the proofs of Lemmas C.2 and C.3 go through without any change.”
Sieve estimation of panel data models with cross section dependence (2012, background): “See Newey (1994, 1995, 1997), Li (2000), Baltagi and Li (2002), and Li et al. (2003), among others.”
Nonparametric Instrumental Variables Estimation (2013, background): “In other nonparametric estimation settings can do this, for both kernel and series estimators, Newey (1994, 1997).”
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).”
Nonparametric specification tests for conditional duration models (2005, background): “In addition, provided that assumption A5 holds, it suffices to plug θ̂ n into θ H f and truncate the infinite sum as in Newey and West (1987) to obtain a consistent estimator of the asymptotic variance.”
Locally Robust Semiparametric Estimation (2016, methodology): “Here ( ) is the plim of ̂ under general misspecification, similar to Newey (1994a), where is unrestricted except for regularity conditions such as existence of ( ) or the expectation of certain functions of the data.”