This paper studies regression models with a lagged dependent variable when both the dependent and independent variables are nonstationary, and the regression model is misspecified in some dimension. In particular, we discuss the limiting properties of least-squares estimates of the parameters in...

This paper provides theoretical properties and Monte-Carlo studies of a stochastic conditional duration model with mixture-of-normal error distributions an effcient estimation approach via a continuous empirical characteristic function. The empirical version of this paper is studied in Xu,...

In this paper we theoretically derive the risk of Zellner's extended minimum expected loss function estimator. Using artificial data, we then calculate the risks of known nested estimators that include simple minimum expected loss function, two stage least squares and ordinary least squares. The...

This paper discusses how conditional heteroskedasticity models can be estimated efficiently without imposing strong distributional assumptions such as normality. Using the generalized method of moments (GMM) principle, we show that for a class of models with a symmetric conditional distribution,...