CROSS-VALIDATED LOCAL LINEAR NONPARAMETRIC REGRESSION

By Qi Li, Jeffrey S. Racine. 2004

490 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/65d6ef19f4465000aeaaa7781f3485c969f433fe

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Citations in the corpus

Local polynomial estimation of nonparametric simultaneous equations models (2008, background): “For the bandwidth selection problem in the case of mixed categorical and continuous data, see the important work by Li and Racine (2004).”
The CO2-Growth nexus revisited: A nonparametric analysis for G7 economies over nearly two centuries (2017, methodology): “To accomplish this, we use the cross-validated Local Linear (LL) nonparametric regression technique developed by Li and Racine (2004) to observe whether there is an inverted-U shaped relationship between CO2 emissions per capita and GDP per capita.”
Nonparametric Identification in Nonseparable Panel Data Models with Generalized Fixed Effects (2009, background): “…can readily accommodate the empirically significant mixed data case in which some variables are continuous and others are discrete (e.g., Li and Racine (2004)), and they permit one to avoid the boundary problems that arise with the use of standard kernel (polynomial of degree zero)…”
A consistent model specification test with mixed discrete and continuous data (2007, unknown): “6 The local linear CV method can detect and remove discrete irrelevant regressors, see Li and Racine (2004) ).”
Non‐parametric methods for doubly robust estimation of continuous treatment effects (2015, methodology): “A)Tg 1(1;0)Th K(0) is the ithdiagonal of the so-called smoothing or hat matrix. The properties of this approach can be derived using logic similar to that in Theorem 3, e.g., by adapting results from Li and Racine (2004). Alternatively one could split the sample, estimate the nuisance functions in o”
Nonparametric Econometrics: The np Package (2008, methodology): “Next, we consider using the local-linear nonparametric method described in Li and Racine (2004).”
Jackknife model averaging (2012, methodology): “We also consider two nonparametric kernel estimators, namely, the local linear (NPll) and local constant (NPlc) estimators with datadriven bandwidths modified to handle categorical and continuous data; see Li and Racine (2004) and Racine and Li (2004) for details.”
The Impact of Homework on Student Achievement (2008, methodology): “To proceed, we utilize Li-Racine Generalized Kernel Estimation (Li and Racine, 2004 and Racine and Li, 2004) and express the test score equation as TSi = θ (xi ) + ei , i = 1, . . . , N , (3.2) where θ (·) is the unknown smooth educational production function, ei is an additive error term and N is t”
Doubly robust uniform confidence band for the conditional average treatment effect function (2016, methodology): “If some covariates are discrete and others are continuous, then one may use a smoothing approach proposed in Li and Racine (2004).”
Is gravity linear (2008, background): “The four measures of forecast accuracy — equations (23) - (26) — are presented in Table 9.”
Volatility jumps: The role of geopolitical risks (2018, methodology): “1 For each quantile, we determine the bandwidth using the leave-one-out least-squares cross validation method of Racine and Li (2004) and Li and Racine (2004).”
The marginal impacts of energy prices on carbon price variations: Evidence from a quantile-on-quantile approach (2021, unknown): “Due to the weaknesses of existing strategies, we use cross-validation (CV) method for the optimal bandwidth selection following Stone (1984); Li and Racine (2004).”
The Relationship between Stock Returns and Volatility in International Stock Markets (2005, unknown): “In nonparametric kernel estimations, Hurvich et al. (1998), Li and Racine (2004), and Racine and Li (2004) show that an improved (a modified) AIC criterion performs well in selecting smoothing parameters.”
Spline Regression in the Presence of Categorical Predictors (2015, methodology): “…has also been used extensively for bandwidth selection for kernel estimators such as the local linear kernel estimator proposed by Li and Racine (2004) that appears in the simulations in Section 4 (see also Racine and Li (2004) for the local constant counterpart), while Hall…”
Tilted Nonparametric Estimation of Volatility Functions With Empirical Applications (2010, methodology): “Figure 3(a) shows a scatterplot and a local linear mean fit with the bandwidth h′ = 5809 chosen via crossvalidation (Li and Racine 2004; see also Li and Racine 2007, p. 93).”