Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/64736
Authors: 
Chen, Xiaohong
Year of Publication: 
2011
Series/Report no.: 
cemmap working paper CWP23/11
Abstract: 
In this selective review, we first provide some empirical examples that motivate the usefulness of semi-nonparametric techniques in modelling economic and financial time series. We describe popular classes of semi-nonparametric dynamic models and some temporal dependence properties. We then present penalized sieve extremum (PSE) estimation as a general method for semi-nonparametric models with cross-sectional, panel, time series, or spatial data. The method is especially powerful in estimating difficult ill-posed inverse problems such as semi-nonparametric mixtures or conditional moment restrictions. We review recent advances on inference and large sample properties of the PSE estimators, which include (1) consistency and convergence rates of the PSE estimator of the nonparametric part; (2) limiting distributions of plug-in PSE estimators of functionals that are either smooth (i.e., root-n estimable) or non-smooth (i.e., slower than root-n estimable); (3) simple criterion-based inference for plug-in PSE estimation of smooth or non-smooth functionals; and (4) root-n asymptotic normality of semiparametric two-step estimators and their consistent variance estimators. Examples from dynamic asset pricing, nonlinear spatial VAR, semiparametric GARCH, and copula-based multivariate financial models are used to illustrate the general results.
Subjects: 
Nonlinear time series
Temporal dependence
Tail dependence
Penalized sieve M estimation
Penalized sieve minimum distance
Semiparametric two-step
Nonlinear ill-posed inverse
Mixtures
Conditional moment restrictions
Nonparametric endogeneity
Dynamic asset pricing
Varying coefficient VAR
GARCH
Copulas
Value-at-risk
JEL: 
C13
C14
C20
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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