The first part of my thesis examines how data on farmland prices can be used to examine the impact of climate change. One of the most vulnerable sectors to a change in climatic conditions is agriculture, where climatic variables such as temperature and precipitation are direct inputs into the production function. Previous studies of the potential impact of global warming on U.S. agriculture have yielded widely varying results, with some predicting large damages and others suggesting that U.S. agriculture may even benefit, in at least one likely scenario associated with a doubling of greenhouse gas concentrations. This thesis shows that much, if not all, of the difference in estimates can be explained by the failure to adequately allow for differences between rain-fed and irrigated agriculture, and proximity to urban areas, in the estimation of the relationship between farmland values and climatic and other variables. A cross-sectional data set of counties in the continental U.S. is employed to estimate a hedonic value function. Special attention is given to modeling the error term structure and it is shown that the derived set of weights are best at explaining the heteroscedasticity and spatial correlation of the error terms. Second, bootstrap simulations are employed to assess the variability of the damage estimator. Third, Chow tests and a Bayesian outlier analysis show that irrigated and urban counties severely bias the damage estimator. When the analysis is limited to dryland and non-urban counties, the different damage estimators from previous studies overlap and the confidence intervals are cut by up to half. Dryland U.S. agriculture is unambiguously damaged under the CO2 doubling scenario, and the damages are quite large relative to recent estimates in the literature.

The second part of my dissertation presents a model for the optimal extraction and exploration of an exhaustible resource with an uncertain stock size. There is an inherent tradeoff in the model. On the one hand, delaying the exploration process as long as possible reduces the discounted exploration cost. On the other hand obtaining earlier information about the amount of consumable reserves contained in the currently unexplored resource stock improves the intertemporal allocation of consumption. The solution implies that exploration should be started as soon as the stock of known reserves falls below a critical level, and should cease when the stock rises above this critical level. As long as there are positive reserves and unexplored resources, the price of reserves fluctuates below a constant maximum. Nevertheless, expected price always rises at the rate of interest, as implied by Hotelling’s rule, yet the distribution of future prices is highly skewed. Numerical simulations confirm that realized prices can with high probability follow a saw-tooth pattern with a common constant finite (local) maximum, until unexplored resources are exhausted.