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...

We examine a simple estimator for the multivariate moving average model based on vector autoregressive approximation. In finite samples the estimator has a bias which is low where roots of the characteristic equation are well away from the unit circle, and more substantial where one or more...

This paper provides a critical survey of the methods employed to model the effects of risk in econometric models. Most of the popular methods are shown to suffer from errors-in-variables bias, and an instrumental variable method is suggested to overcome this problem. The technique exploits the...

In this paper a semiparametric fixed effect estimator is proposed in a partial linear model. The estimator is applied to study the immigrants home-link effect on the U.S. producer trade flows with the home country of the immigrant. The results from this study show that the immigrants support the...

A unified framework for various nonparametric kernel regression estimators is presented, based on which we consider two nonparametric tests for neglected nonlinearity in time series regression models. One of them is the goodness-of-fit test of Cai, Fan, and Yao (2000) and another is the...

We derive some new results on the expectation of quadratic forms in normal and nonnormal variables. Using a nonstochastic operator, we show that the expectation of the product of an arbitrary number of quadratic forms in noncentral normal variables follows a recurrence formula. This formula...