(i) Policy Models

The modeling methods below are developed with agricultural and environmental policy models in mind. Such models do not test fundamental properties of the system such as the rate of technical change or the presence of curvature in the profit function. However, policy models do use the information from more aggregate models to develop disaggregate models that can reproduce observed resource use, and forecast the reaction of farmers and resource users to changes in prices, property rights, or environmental constraints on resource use and disposal.

(ii) Disaggregated economic modeling methods

I am currently developing reconstruction and calibration methods based on Maximum Entropy estimators to model the economic structure of farming and other economic uses of land and resources from disaggregated data on land area, yield and crop selection. In short, instead of using economic survey data to infer the use of resources, I am using physical data to infer the underlying economic functions facing the manager. The reasons for this approach are (a) Environmental analysis must be done on a disaggregated level to be meaningful, and (b) Remote sensing methods can supply physical data at a fraction of the cost of economic surveys.

(iii) Empirical dynamic stochastic methods

These approaches can be used to analyze the switch in investments and changes in institutions subject to dynamic stochastic inputs and irreversible costs or decisions.