The equity premium, return on equity minus return on risk-free asset, is expected to be positive. We consider imposing such positivity constraint in local historical average (LHA) in nonparametric kernel regression framework. It is also extended to the semiparametric single index model when...

This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang (2001). Asymptotic properties of our proposed estimators and forecasts are established. Monte Carlo simulation is conducted to...

This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang (2001). Asymptotic properties of our proposed estimators and forecasts are established. Monte Carlo simulation is conducted to...

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...

The bulk of the literature on retail location looks at the topic from the perspective of either the retail firm or the individual shopper. Another branch of the literature examines the spatial distribution of retail activities within a city or region, drawing on either central place theory or...

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...