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Consistent testing for stochastic dominance under general sampling schemes

Linton, Oliver; Maasoumi, Esfandiar; Whang, Yoon-Jae - London School of Economics (LSE) - 2003

Persistent link: https://www.econbiz.de/10010928652

Nonparametric inference for unbalanced time series data

Linton, Oliver - London School of Economics (LSE) - 2004

data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a standard HAC/Bootstrap …

Persistent link: https://www.econbiz.de/10010746385

Inference about realized volatility using infill subsampling

Kalnina, Ilze; Linton, Oliver - London School of Economics (LSE) - 2007

We investigate the use of subsampling for conducting inference about the quadratic variation of a discretely observed … diffusion process under an infill asymptotic scheme. We show that the usual subsampling method of Politis and Romano (1994) is … inconsistent when applied to our inference question. Recently, a type of subsampling has been used to do an additive bias …

Persistent link: https://www.econbiz.de/10010928783

Consistent testing for stochastic dominance : a subsampling approach

Linton, Oliver; Maasoumi, Esfandiar; Whang, Yoon-Jae - London School of Economics (LSE) - 2002

conditional ranking. We also propose a test of Prospect Stochastic Dominance. Our method is based on subsampling and we show that …

Persistent link: https://www.econbiz.de/10010746327

Are there Monday effects in stock returns: a stochastic dominance approach

Cho, Young-Hyun; Linton, Oliver; Whang, Yoon-Jae - London School of Economics (LSE) - 2006

We provide a test of the Monday effect in daily stock index returns. Unlike previous studies we define the Monday effect based on the stochastic dominance criterion. This is a stronger criterion than those based on comparing means used in previous work and has a well defined economic meaning. We...

Persistent link: https://www.econbiz.de/10010746600

Estimation of large precision matrices through block penalization

Lam, Clifford - London School of Economics (LSE) - 2008

This paper focuses on exploring the sparsity of the inverse covariance matrix $\bSigma^{-1}$, or the precision matrix. We form blocks of parameters based on each off-diagonal band of the Cholesky factor from its modified Cholesky decomposition, and penalize each block of parameters using the...

Persistent link: https://www.econbiz.de/10010745777

Sparsistency and rates of convergence in large covariance matrix estimation

Lam, Clifford; Fan, Jianqing - London School of Economics (LSE) - 2009

This paper studies the sparsistency and rates of convergence for estimating sparse covariance and precision matrices based on penalized likelihood with nonconvex penalty functions. Here, sparsistency refers to the property that all parameters that are zero are actually estimated as zero with...

Persistent link: https://www.econbiz.de/10011071205

On determination of cointegration ranks

Li, Qiaoling; Pan, Jiazhu; Yao, Qiwei - London School of Economics (LSE) - 2009

We propose a new method to determine the cointegration rank in the error correction model of Engle and Granger (1987). To this end, we first estimate the cointegration vectors in terms of a residual-based principal component analysis. Then the cointegration rank, together with the lag order, is...

Persistent link: https://www.econbiz.de/10010746018

The existence and asymptotic properties of a backfitting projection algorithm under weak conditions

Mammen, Enno; Linton, Oliver; Nielsen, J - London School of Economics (LSE) - 2000

We derive the asymptotic distribution of a new backfitting procedure for estimating the closest additive approximation to a nonparametric regression function. The procedure employs a recent projection interpretation of popular kernel estimators provided by Mammen, Marron, Turlach and Wand...

Persistent link: https://www.econbiz.de/10010744974

A nonparametric regression estimator that adapts to error distribution of unknown form

Linton, Oliver; Xiao, Zhijie - London School of Economics (LSE) - 2001

We propose a new estimator for nonparametric regression based on local likelihood estimation using an estimated error score function obtained from the residuals of a preliminary nonparametric regression. We show that our estimator is asymptotically equivalent to the infeasible local maximum...

Persistent link: https://www.econbiz.de/10010745013