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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
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Quasi-maximum likelihood estimation for long memory stock transaction data - under conditional heteroskedasticity framework
Quoreshi, A. M. M. Shahiduzzaman; Uddin, Reaz; Khan, … - In: Journal of risk and financial management : JRFM 12 (2019) 2/74, pp. 1-13
distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that …
Persistent link: https://www.econbiz.de/10012022130
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Efficient estimation in heteroscedastic varying coefficient models
Wei, Chuanhua; Wan, Lijie - In: Econometrics : open access journal 3 (2015) 3, pp. 525-531
This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator. We establish asymptotic normality for the proposed estimator and conduct...
Persistent link: https://www.econbiz.de/10011297551
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Heteroscedastic Analysis in Nonlinear Modeling
Louangrath, Paul - 2013
nonlinear model. At the heart of nonlinear modeling is the concept of heteroscedasticity. Heteroscedasticity refers to the non … presence of heteroscedasticity may cast doubt on the proposed model if the noise from Xt is big; otherwise, it is no cause for … concern. If the model is a good model, the existence of heteroscedasticity among the independent variable would not vitiate …
Persistent link: https://www.econbiz.de/10013076070
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Statistics of Heteroscedastic Extremes
Einmahl, John H. J. - 2014
We extend classical extreme value theory to non-identically distributed observations. When the distribution tails are proportional much of extreme value statistics remains valid. The proportionality function for the tails can be estimated nonparametrically along with the (common) extreme value...
Persistent link: https://www.econbiz.de/10013058580
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Power Maximization and Size Control in Heteroskedasticity and Autocorrelation Robust Tests with Exponentiated Kernels
Sun, Yixiao - 2010
Using the power kernels of Phillips, Sun and Jin (2006, 2007), we examine the large sample asymptotic properties of the t-test for different choices of power parameter (rho). We show that the nonstandard fixed-rho limit distributions of the t-statistic provide more accurate approximations to the...
Persistent link: https://www.econbiz.de/10013148975
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A New Asymptotic Theory for Vector Autoregressive Long-Run Variance Estimation and Autocorrelation Robust Testing
Sun, Yixiao; Kaplan, David - 2010
In this paper, we develop a new asymptotic theory of the long run variance estimator obtained by fitting a vector autoregressive model to the transformed moment processes in a GMM framework. In contrast to the conventional asymptotics where the VAR lag order p goes to infinity but at a slower...
Persistent link: https://www.econbiz.de/10014188745
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Let's Fix it : Fixed-b Asymptotics Versus Small-b Asymptotics in Heteroscedasticity and Autocorrelation Robust Inference
Sun, Yixiao - 2010
In the presence of heteroscedasticity and autocorrelation of unknown forms, the covariance matrix of the parameter …
Persistent link: https://www.econbiz.de/10014188747
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Consistent Hac Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation
Phillips, Peter C. B.; Sun, Yixiao; Jin, Sainan - 2003
A new family of kernels is suggested for use in heteroskedasticity and autocorrelation consistent (HAC) and long run variance (LRV) estimation and robust regression testing. The kernels are constructed by taking powers of the Bartlett kernel and are intended to be used with no truncation (or...
Persistent link: https://www.econbiz.de/10014088395
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How much should we trust Regression-Kink-Design estimates?
Ando, Michihito - 2013
In a Regression Kink (RK) design with a finite sample, a confounding smooth nonlinear relationship between an assignment variable and an outcome variable around a threshold can be spuriously picked up as a kink and result in a biased estimate. In order to investigate how well RK designs handle...
Persistent link: https://www.econbiz.de/10010211390