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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

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

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

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

Panel nonparametric regression with fixed effects

Lee, Jungyoon; Robinson, Peter - London School of Economics (LSE) - 2015

Nonparametric regression is developed for data with both a temporal and a cross-sectional dimension. The model includes additive, unknown, individual-specific components and allows also for cross-sectional and temporal dependence and conditional heteroscedasticity. A simple nonparametric...

Persistent link: https://www.econbiz.de/10011268330

Series estimation under cross-sectional dependence

Lee, Jungyoon; Robinson, Peter M. - London School of Economics (LSE) - 2013

An asymptotic theory is developed for nonparametric and semiparametric series estimation under general cross-sectional dependence and heterogeneity. A uniform rate of consistency, asymptotic normality, and sufficient conditions for convergence, are established, and a data-driven studentization...

Persistent link: https://www.econbiz.de/10011126210