Graphical modeling of stochastic processes driven by correlated errors

We study a class of graphs that represent local independence structures in stochastic processes allowing for correlated error processes. Several graphs may encode the same local independencies and we characterize such equivalence classes of graphs. In the worst case, the number of conditions in our characterizations grows superpolynomially as a function of the size of the node set in the graph. We show that deciding Markov equivalence is coNP-complete which suggests that our characterizations cannot be improved upon substantially. We prove a global Markov property in the case of a multivariate Ornstein-Uhlenbeck process which is driven by correlated Brownian motions.Comment: 43 page

The Dynamics of Farm Land Allocation – Short and Long Run Reactions in a Long Micro Panel

This study develops a dynamic multi-output model of farmers’ crop allocation decisions that allows estimation of both short-run and long-run adjustments to a wide array of economic incentives. The method can be used to inform decision-makers about a number of issues including agricultural policy reform and environmental regulation. The model allows estimation of dynamic effects relating to price expectations adjustment, investment lags and crop rotation constraints. Estimation is based on micro-panel data from Danish farmers that includes acreage, output and variable input utilisation at the crop level. Results indicate that there are substantial differences between the shortrun and long-run land allocation behaviour of Danish farmers and that there are substantial differences in the time lags associated with different crops. Since similar farming conditions are found in northern Europe and parts of the USA and Canada, this result may have more general interest.land allocation, crop rotation, system of dynamic equations, micro panel data, GMM