We extend the analysis of Christoffersen and Diebold (1998) on long-run forecasting in cointegrated systems to multicointegrated systems. For the forecast evaluation we consider several loss functions, each of which has a particular interpretation in the context of stock-flow models where...

We extend the analysis of Christoffersen and Diebold (1998) on long-run forecasting in cointegrated systems to multicointegrated systems. For the forecast evaluation we consider several loss functions, each of which has a particular interpretation in the context of stock-flow models where...

In this paper long-run forecasting of multicointegrating variables is investigated. Multicointegration typically occurs in dynamic systems involving both stock and flow variables whereby a common feature in the form of shared stochastic trends is present across different levels of multiple time...

In this paper long-run forecasting of multicointegrating variables is investigated. Multicointegration <p> typically occurs in dynamic systems involving both stock and flow variables whereby a common feature <p> in the form of shared stochastic trends is present across different levels of multiple...</p></p>

This paper derives a method for estimating and testing the Linear Quadratic Adjustment Cost (LQAC) model when the target variable and some of the forcing variables follow I(2) processes. Based on a forward-looking error-correction formulation of the model it is shown how to obtain strongly...

Multicointegration, in the sense of Granger and Lee (1990), frequently occurs in models of stock-flow adjustment and implies cointegration amongst I(2) variables and their differences (polynomial cointegration). The purpose of this article is two-fold. First, we demonstrate that based on a...

This paper derives a method for estimating and testing the Linear Quadratic Adjustment Cost (LQAC) model when the target variable and some of the forcing variables follow I(2) processes. Based on a forward-looking error-correction formulation of the model it is shown how to obtain strongly...