Machine Learning and Econometrics for Agricultural Policy

The project seeks to evaluate the implied errors or shocks to the future "history" of economic variables from a set of policy restrictions. That is to say, the project will compare the implied path from a set of agricultural policies with the historical observed errros in order to assess how plausible or likely it is that the implied paths could ever occur. Based on historical measures of theory supported price, quantity and instrumental variables observed over recent time, we fit a dynamic model and project forward months and years into the future the size of the errors on all series in the dynamic model. These errors are then compared with already known errors from previous years and months to assess the likely realization of the particultura policy. If large errors are required, the policy analyst might reasses the magnitude of the policy restriction or may offer additional evidence supporting why the relatively large errors are likely to be forthcoming.

Machine Learning and Econometrics for Agricultural Policy Grant