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Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models
Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; semi-nonparametric models are more flexible and robust, but lead to otherExpand
Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions
We propose an estimation method for models of conditional moment restrictions, which contain finite dimensional unknown parameters (theta) and infinite dimensional unknown functions (h). Our proposalExpand
MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS
This paper first provides some useful results on a generalized random coefficient autoregressive model and a generalized hidden Markov model. These results simultaneously imply strict stationarity,Expand
Estimation of Copula-Based Semiparametric Time Series Models
This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric invariant (or marginal) distributions andExpand
Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification
Recently Chen and Fan (2003a) introduced a new class of semiparametric copula-based multivariate dynamic (SCOMDY) models. A SCOMDY model specifies the conditional mean and the conditional variance ofExpand
Semi‐Nonparametric IV Estimation of Shape‐Invariant Engel Curves
This paper studies a shape-invariant Engel curve system with endogenous total expenditure, in which the shape-invariant specification involves a common shift parameter for each demographic group in aExpand
Estimation of Semiparametric Models When the Criterion Function is Not Smooth
We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standardExpand
Sieve extremum estimates for weakly dependent data
Many non/semiparametric time series estimates may be regarded as different forms of sieve extremum estimates. For stationary absolute regular mixing observations, the authors obtain convergence ratesExpand
Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions
We show that spline and wavelet series regression estimators for weakly dependent regressors attain the optimal uniform (i.e. sup-norm) convergence rate (n/logn)−p/(2p+d) of Stone (1982), where d isExpand
Semiparametric efficiency in GMM models with auxiliary data
We study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing data. Identification relies on auxiliary data containingExpand
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