Suppose that a target function is monotonic, namely, weakly increasing, and an original estimate of this target function is available, which is not weakly increasing. Many common estimation methods used in statistics produce such estimates. We show that these estimates can always be improved...

We propose a least squares regression framework for the estimation of the realized covariation matrix using high frequency data. The new estimator is robust to market microstructure noise (MMS) and non-synchronous trading. Comprehensive simulation and empirical analysis show that our estimator...

This paper analyses multivariate high frequency financial data using realised covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis and covariance. It will be based on a fixed interval of time (e.g. a day or week), allowing the...

Scale mixtures of uniform distributions are used to model non-normal data in both univariate and multivariate settings. In addition to providing greater modelling flexibility, the use of scale mixtures of uniforms also results in straightforward computational strategies, particularly in a...

Recent empirical studies in regional science and urban economics show that agglomeration economies may be one source of the uneven distribution of economic activities and economic growth across cities and regions. At the same time, the body of research into the importance of agglomeration...

In a high dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically, we employ the partial covariances between the response variable and the tested covariates to obtain a test statistic. The resulting...