The Probability of Information (PIN)-based trading introduced by Easley <italic>et al</italic>. (1996, 2002) has been adopted to address a variety of issues in empirical finance. To obtain PIN using numerical Maximum Likelihood Estimation (MLE) from transaction data, one may suffer from the numerical overflow or...

Dynamic portfolio choice has been a central and essential objective for institutional investors in active asset management. In this paper, we study the dynamic portfolio choice with multiple conditioning variables, where the number of the conditioning variables can be either fixed or diverging...

Local linear fitting is a popular nonparametric method in nonlinear statistical and econometric modelling. Lu and Linton (2007) established the point wise asymptotic distribution (central limit theorem) for the local linear estimator of nonparametric regression function under the condition of...

We propose to approximate the conditional expectation of a spatial random variable given its nearest-neighbour observations by an additive function. The setting is meaningful in practice and requires no unilateral ordering. It is capable of catching nonlinear features in spatial data and...

We have established the asymptotic theory for the estimation of adaptive varying-coe�cient linear models. More speci�cally we have shown that the estimator for the global index parameter is root-n consistent without imposing, as a prerequisite, that the estimator is within n

We consider approximating a multivariate regression function by an affine combination of one-dimensional conditional component regression functions. The weight parameters involved in the approximation are estimated by least squares on the first-stage nonparametric kernel estimates. We establish...

Local linear fitting is a popular nonparametric method in statistical and econometric modeling. Lu and Linton (<xref>2007</xref>, <italic>Econometric Theory</italic>23, 37–70) established the pointwise asymptotic distribution for the local linear estimator of a nonparametric regression function under the condition of near...

We consider approximating a multivariate regression function by an affine combination of one-dimensional conditional component regression functions. The weight parameters involved in the approximation are estimated by least squares on the first-stage nonparametric kernel estimates. We establish...