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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
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Penalized sieve estimation and inference of semi-nonparametric dynamic models : a selective review
Chen, Xiaohong - 2011
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. -- Nonlinear time … ; Semiparametric two-step ; Nonlinear ill-posed inverse ; Mixtures ; Conditional moment restrictions ; Nonparametric endogeneity …
Persistent link: https://www.econbiz.de/10009230387
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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 … Screening (KSIS) technique for the nonlinear time series setting which screens out the regressors whose marginal regression (or …-high dimensional exogenous regressors and use a well-known principal component analysis to estimate the latent common factors, and then …
Persistent link: https://www.econbiz.de/10011343005
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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
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A flexible semiparametric model for time series
Li, Degui; Linton, Oliver; Lu, Zu-di - 2012
time series analysis. Furthermore, the methods and results are augmented by a simulation study and illustrated by … application in the analysis of the Australian annual mean temperature anomaly series. We also apply our methods to high frequency …
Persistent link: https://www.econbiz.de/10009620324
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Let's get LADE : robust estimation of semiparametric multiplicative volatility models
Koo, Bonsoo; Linton, Oliver - 2013
We investigate a model in which we connect slowly time varying unconditional long-run volatility with short-run conditional volatility whose representation is given as a semi-strong GARCH (1,1) process with heavy tailed errors. We focus on robust estimation of both long-run and short-run...
Persistent link: https://www.econbiz.de/10009719116
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Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions
Chen, Xiaohong; Christensen, Timothy M. - 2014 - Rev.
nonlinear, irregular functionals of the conditional mean function under weak conditions. The results are proved by deriving a …
Persistent link: https://www.econbiz.de/10010458629
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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
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Efficient estimation of copula-based semiparametric Markov models
Chen, Xiaohong; Wu, Wei Biao; Yi, Yanping - 2009
having strong tail dependence -- Copula ; Tail dependence ; Nonlinear Markov models ; Geometric ergodicity ; Sieve MLE …
Persistent link: https://www.econbiz.de/10003817253
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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