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Recent Developments in Non- and Semiparametric Regression with Fractional Time Series Errors
Beran, Jan; Feng, Yuanhua - 2002
This paper summarizes recent developments in non- and semiparametric regres- sion with stationary fractional time series errors, where the error process may be short-range, long-range dependent or antipersistent. The trend function in this model is estimated nonparametrically, while the...
Persistent link: https://www.econbiz.de/10010324094
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Recent developments in non- and semiparametric regression with fractional time series errors
Beran, Jan; Feng, Yuanhua - 2002
This paper summarizes recent developments in non- and semiparametric regression with stationary fractional time series …
Persistent link: https://www.econbiz.de/10011544974
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Recent developments in non- and semiparametric regression with fractional time series errors
Beran, Jan; Feng, Yuanhua - 2002
Persistent link: https://www.econbiz.de/10001686441
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High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
Chang, Jinyuan; Guo, Bin; Yao, Qiwei - In: Journal of econometrics 189 (2015) 2, pp. 297-312
Persistent link: https://www.econbiz.de/10011504536
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S-estimation in the nonlinear regression model with long-memory error terms
Sibbertsen, Philipp - 1999
Persistent link: https://www.econbiz.de/10001439129
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Estimating the persistence and the autocorrelation function of a time series that this measured with error
Hansen, Peter Reinhard; Lunde, Asger - 2010
Persistent link: https://www.econbiz.de/10003934448
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On robust local polynomial estimation with long-memory errors
Beran, Jan; Feng, Yuanhua; Gosh, Sucharita; Sibbertsen, … - 2000
. However, outliers may lead to unreliable estimates, if least squares regression is used. In this paper, local polynomial …
Persistent link: https://www.econbiz.de/10010324063
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Local Polynomial Estimation with a FARIMA-GARCH Error Process
Beran, Jan; Feng, Yuanhua - 1999
Persistent link: https://www.econbiz.de/10010324074
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Nonparametric M-Estimation with Long-Memory Errors
Beran, Jan; Gosh, Sucharita; Sibbertsen, Philipp - 2000
We investigate the behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind...
Persistent link: https://www.econbiz.de/10010324084
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Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent errors
Beran, Jan; Feng, Yuanhua - 1999
Nonparametric regression with long-range and antipersistent errors is considered. Local polynomial smoothing is … (with a fractional differencing parameter 0 < d < 1/2), nonparametric regression estimators converge at a slower rate than …/2 < d < 0), the convergence rate of a nonparametric regression estimator is faster than for uncorrelated or short …
Persistent link: https://www.econbiz.de/10010324088