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Let's get LADE : robust estimation of semiparametric multiplicative volatility models

Koo, Bonsoo; Linton, Oliver - 2013

robust estimation of both long-run and short-run volatilities. Our estimation is semiparametric since the long-run volatility …

Persistent link: https://www.econbiz.de/10009719116

Penalized sieve estimation and inference of semi-nonparametric dynamic models : a selective review

Chen, Xiaohong - 2011

-based inference for plug-in PSE estimation of smooth or non-smooth functionals; and (4) root-n asymptotic normality of semiparametric …, semiparametric GARCH, and copula-based multivariate financial models are used to illustrate the general results. -- Nonlinear time … ; Semiparametric two-step ; Nonlinear ill-posed inverse ; Mixtures ; Conditional moment restrictions ; Nonparametric endogeneity …

Persistent link: https://www.econbiz.de/10009230387

Semiparametric model averaging of ultra-high dimensional time series

Chen, Jia; Li, Degui; Linton, Oliver; Lu, Zu-di - 2015

In this paper, we consider semiparametric model averaging of the nonlinear dynamic time series system where the number … forecast the response variable, we propose two semiparametric approaches of dimension reduction among the exogenous regressors … smaller than the sample size; then we consider a semiparametric method of Model Averaging MArginal Regression (MAMAR) for the …

Persistent link: https://www.econbiz.de/10011343005

Non-parametric transformation regression with non-stationary data

Linton, Oliver; Wang, Qiying - 2013

We examine a kernel regression smoother for time series that takes account of the error correlation structure as proposed by Xiao et al. (2008). We show that this method continues to improve estimation in the case where the regressor is a unit root or near unit root process.

Persistent link: https://www.econbiz.de/10009734305

A flexible semiparametric model for time series

Li, Degui; Linton, Oliver; Lu, Zu-di - 2012

We consider approximating a multivariate regression function by an affine combination of one-dimensional conditional component regression functions. The weight parameters involved in the approximation are estimated by least squares on the first-stage nonparametric kernel estimates. We establish...

Persistent link: https://www.econbiz.de/10009620324

Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions

Chen, Xiaohong; Christensen, Timothy M. - 2014 - Rev.

We show that spline and wavelet series regression estimators for weakly dependent regressors attain the optimal uniform (i.e. sup-norm) convergence rate (n= log n)..p=(2p+d) of Stone (1982), where d is the number of regressors and p is the smoothness of the regression function. The optimal rate...

Persistent link: https://www.econbiz.de/10010458629

Estimation of stochastic volatility models by nonparametric filtering

Kanaya, Shin; Kristensen, Dennis - 2015

A two-step estimation method of stochastic volatility models is proposed. In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the...

Persistent link: https://www.econbiz.de/10010487528

Efficient estimation of copula-based semiparametric Markov models

Chen, Xiaohong; Wu, Wei Biao; Yi, Yanping - 2009

This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These … ; Semiparametric efficiency ; Sieve likelihood ratio statistics ; Value-at-Risk …

Persistent link: https://www.econbiz.de/10003817253

Kernel block bootstrap

Parente, Paulo M. D. C.; Smith, Richard J. - 2018 - This draft: July 2018

This article introduces and investigates the properties of a new bootstrap method for time-series data, the kernel block bootstrap. The bootstrap method, although akin to, offers an improvement over the tapered block bootstrap of Paparoditis and Politis (2001), admitting kernels with unbounded...

Persistent link: https://www.econbiz.de/10011878210

Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction

Cheng, Tingting; Gao, Jiti; Linton, Oliver - 2018

In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive...

Persistent link: https://www.econbiz.de/10011775136