Sieve extremum estimates for weakly dependent data

By Xiaohong Chen, Xiaotong Shen. 1998

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Citations in the corpus

Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning (2019, methodology): “Given regularity conditions and smoothness conditions on q , we can obtain rates on (cid:107) q ( · , · ; ˆ β N ) − q (cid:107) 2 without assuming correct parametric speciﬁcation (Chen and Shen, 1998).”
Machine Learning Advances for Time Series Forecasting (2020, background): “Their setup is defined as follows: where 𝑓(𝑿 𝑡 ) is a nonlinear function that is nonparametrically modeled by sieve approximations.”
Convolutional autoregressive models for functional time series (2016, unknown): “1 Introduction Functional data analysis has received much attention over the last few decades, and has been widely applied in many fields, including medical science (Houghton, Flannery, and Vola, 1980; Gasser, Müller, Köhler, Molinari, and Prader, 1984; Ratcliffe, Leader, and Heller, 2002a; Ratcliff”
Conditional quantile processes based on series or many regressors (2011, methodology): “[13] Xiaohong Chen and Xiaotong Shen.”
Characterization of the asymptotic distribution of semiparametric M-estimators (2006, background): “(1995), Chen and Shen (1998) , Ai and Chen (2003), Chen, Linton, and Van Keilegom”
Estimation of Nonparametric Conditional Moment Models with Possibly Nonsmooth Generalized Residuals (2011, background): “10 See Chen (2007) and the online supplemental material for definitions of Hölder space, Sobolev space, Besov space and other widely used function spaces in economics.”
Massachusetts Institute of Technology Department of Economics Working Paper Series Conditional Quantile Processes Based on Series or Many Regressors Conditional Quantile Processes Based on Series or Many Regressors (unknown): “In particular, we rely on the work of Stone [35], Andrews [2], Newey [29], Chen and Shen [13], Chen [12] and others that rigorously motivated the series framework as an approximation scheme and gave pointwise normality results for least squares estimators, and on Chen [12] and van de Geer [36] that ”
Generalised Density Forecast Combinations (2015, unknown): “It proposes the use of sieve estimation (cf. Chen & Shen (1998)) as a means of optimising the score of the generalised density combination over a tractable, approximating space of weight functions on the component densities.”
Series estimation under cross-sectional dependence (2013, background): “For weakly dependent time series data, Chen and Shen (1998) and Chen et al. (2012) offer a rather complete treatment of asymptotic theory and robust inference of general sieveM-estimation,while Chen and Christensen (2015) shows that spline and wavelet series regression estimation obtains the…”
Semi‐Nonparametric IV Estimation of Shape‐Invariant Engel Curves (2003, methodology): “In our application we shall also present fully nonparametric estimates of the model of the Engel curves as given in (2.3): Y1l = Gl(Y2,X1) + εl for l = 1, ..., L; this is 15This is the approach taken in Chen and Shen (1998) and in the old version of Ai and Chen (2003).”
Nonparametric quasi-maximum likelihood estimation for Gaussian locally stationary processes (2006, methodology): “re some contrast functional is minimized over an inﬁnite-dimensional parameter space, including maximum likelihood estimation, M-estimation, least squares estimates in nonparametric regression (e.g., [2, 3, 7, 21, 22]). The theory for all of these approaches is based on the behavior of some kind of ”
Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals (2009, background): “It is previously known that sieve M-estimation of semiparametric models (without nonparametric endogeneity) can si- multaneously achieve root-n normality of parametric part and optimal convergence rate of nonparametric part; see e.g., Chen and Shen (1998) and Newey et al. (2004).”
A new semiparametric spatial model for panel time series (2001, methodology): “We estimate both using sieve estimators whose large sample properties are studied by Chen and Shen (1998) .”
Locally Robust Semiparametric Estimation (2016, methodology): “onfer the small bias property on moment functions that are not locally robust, including series estimators of mean-square projections (Newey, 1994), sieve 2 maximum likelihood estimators (Shen, 1996, Chen and Shen, 1997), bootstrap bias corrected ﬁrst steps (NHR), and higher-order kernels (Bickel an”
Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models (2007, methodology): “9 in Chen and White (1999) for ANN sieve estimation of log-densities and square-root densities, and by the results in Chen and Shen (1998) for linear sieve estimation of squared root-density, we have the following two results:”