In this paper we develop nonparametric estimators of the joint time series data generating process (DGP) of (<italic>x</italic>, <italic>y</italic>) at different <italic>t</italic>-values, of conditional DGP, of the conditional mean of <italic>x</italic> given the past values of <italic>x</italic> and <italic>y</italic>, and, more generally, the conditional mean of (<italic>x</italic>, <italic>y</italic>) given their past values...

In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R²) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being...

We investigate the finite-sample bias of the quasi-maximum likelihood estimator (QMLE) in spatial autoregressive models with possible exogenous regressors. We derive the approximate bias result of the QMLE in terms of model parameters and also the moments (up to order 4) of the error...

We study the finite-sample bias and mean squared error, when properly defined, of the sample coefficient of variation under a general distribution. We employ a Nagar-type expansion and use moments of quadratic forms to derive the results. We find that the approximate bias depends on not only the...