Acreage Abandonment, Moral Hazard and Crop Insurance

Empirical evidence for the existence of moral hazard in the U.S. crop insurance program has been inconclusive. Here we use a nested-dynamic programming framework to estimate an intra-seasonal dynamic model that explicitly incorporates a farmer's crop abandonment decision. The estimation is implemented for selected Texas counties where actuarial performances of the crop insurance program are poor and high incidences of acreage abandonment are frequently observed.Farm Management,

Alternative Methods of Calculating Optimal Timber Rotations: A Critique of the Stokey/Lucas/Prescott Tree-Cutting Problem

The traditional question of optimally deciding when to cut down a tree is among the most commonly posed questions asked of students learning the technique of dynamic programming. This paper shows that the traditional tree-cutting example is improperly formulated when the question of replanting is addressed, derives the proper method of finding optimal harvest length, and applies this method to an empirical forest growth function.forestry, dynamic programing, tree cutting problem

On Optimal Multiple Changepoint Algorithms for Large Data

There is an increasing need for algorithms that can accurately detect changepoints in long time-series, or equivalent, data. Many common approaches to detecting changepoints, for example based on penalised likelihood or minimum description length, can be formulated in terms of minimising a cost over segmentations. Dynamic programming methods exist to solve this minimisation problem exactly, but these tend to scale at least quadratically in the length of the time-series. Algorithms, such as Binary Segmentation, exist that have a computational cost that is close to linear in the length of the time-series, but these are not guaranteed to find the optimal segmentation. Recently pruning ideas have been suggested that can speed up the dynamic programming algorithms, whilst still being guaranteed to find true minimum of the cost function. Here we extend these pruning methods, and introduce two new algorithms for segmenting data, FPOP and SNIP. Empirical results show that FPOP is substantially faster than existing dynamic programming methods, and unlike the existing methods its computational efficiency is robust to the number of changepoints in the data. We evaluate the method at detecting Copy Number Variations and observe that FPOP has a computational cost that is competitive with that of Binary Segmentation.Comment: 20 page