Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models

By Xiaohong Chen. 2007

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Subfields: Artificial Intelligence, Finance, Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Learning When-to-Treat Policies (2019, background): “The problem of learning optimal decision rules is also closely related to learning heterogeneous treatment effects (Athey and Imbens, 2016; Athey, Tibshirani, and Wager, 2019; Chen, 2007; Künzel, Sekhon, Bickel, and Yu, 2019; Nie and Wager, 2017; Wager and Athey, 2018).”
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, background): “For estimating µ k , one can leverage the following relationship to reduce it to a regression problem: (5) (Chen, 2007).”
Nonparametric Tests for Treatment Effect Heterogeneity (2008, neutral): “1998; Imbens, Newey and Ridder, 2006; Chen, Hong, and Tarozzi, 2004; Chen 2005 ).”
Efficient Policy Learning (2017, methodology): “Finally, we also note a growing literature on estimating conditional average treatment effects (Athey and Imbens, 2016; Athey, Tibshirani, and Wager, 2019; Chen, 2007; Künzel, Sekhon, Bickel, and Yu, 2017; Nie and Wager, 2017; Wager and Athey, 2018).”
Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects (2008, neutral): “Applying theorem 3 in Chen and Shen (1998) with their δn = (n) −γ/(2γ+dx), we have”
Semiparametric Single-Index Panel Data Models with Cross-Sectional Dependence (2015, neutral): “Very detailed literature reviews on the Hermite polynomials and the sieve estimation can be seen in Nevai (1986) and Chen (2007) respectively.”
ON THE ASYMPTOTIC EFFICIENCY OF GMM (2013, neutral): “This includes wavelets, splines, and sieves in general (see Chen, 2007).”
Matching for Balance (2019, background): “They are satisfied by many classes of basis functions including regression spline, trigonometric polynomial, and wavelet bases (Newey 1997, Horowitz et al. 2004, Chen 2007, Belloni et al. 2015, Fan et al. 2016).”
Inference in Nonparametric Instrumental Variables With Partial Identification (2012, methodology): “The functional space Θ is defined to be: Θ = {θ(x) : ||θ||S ≤ B} (9)”
On the Identification of Gross Output Production Functions (2020, methodology): “This dataset has been used in several studies, including Roberts and Tybout (1997), Clerides et al. (1998), and Das et al. (2007). The second dataset comes from the census of Chilean manufacturing plants conducted by Chile’s Instituto Nacional de Estadística (INE). It covers all firms from 1979-1996”
Inference in Linear Regression Models with Many Covariates and Heteroscedasticity (2015, methodology): “For further technical details on series-based methods see, for example, Newey (1997), Chen (2007), Cattaneo and Farrell (2013), and Belloni et al. (2015), and references therein.”
Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing (2014, methodology): “, Newey (1994) or Chen, Linton, and van Keilegom (2003). The standard approach next requires showing that p nV 0(b 0) is asymptotically normal, often by requiring that it has a linear representation and that, for C 0; p n jEZn [s(Y;X; b ) s(Y;X; 0)] V 0(b 0)j nC kb 0k(2) (7) = oP (1).”
A simple nonparametric approach to estimating the distribution of random coefficients in structural models (2016, neutral): “Our estimator for models with homogeneous parameters is defined as (similarly to (6))”
Random Coefficients on Endogenous Variables in Simultaneous Equations Models (2014, methodology): “These conditions are essentially just those of Chen et al. (2011), with additional assumptions on the parameter space specific to the estimation problem considered here.”
Varying Coefficient Regression Models: A Review and New Developments (2015, background): “For an overview on the asymptotic theory of sieve estimation in semiparametric models, see Chen (2007).”