We study the e®ect of privately informed traders on measured high frequency price changes and trades in asset markets. We use a standard market microstructure framework where exogenous news is captured by signals that informed agents receive. We show that the entry and exit of informed traders following the arrival of news accounts for high-frequency serial correlation in squared price changes (stochastic volatility) and grades. Because the bid-ask spread of the market specialist tends to shrink as individuals trade and reveal their information, the model also accounts for the empirical observation that high-frequency serial correlation is more pronounced in trades than in squared price changes. A calibration test of the model shows that the features of the market microstructure, without serially correlated news, accounts qualitatively for the serial correlation in the data, but predicts less persistence than is present in the data.

A standard assumption in integrated assessment models of climate change is that population and productivity are growing, but at a decreasing rate. We explore the signifcance of the assumption of population and productivity growth for greenhouse gas abatement. After all, there has been no long run slow down in the growth of productivity over the past few centuries, and the rate of population growth has actually been increasing for the past 19 centuries. Even if either of these growth rates were expected to slow, by how much is subject to great uncertainty. We show computationally that such continued growth greatly increases the severity of climate change. Indeed we nd that climate change is a problem in large part caused by exogenous population and productivity growth. Rapid reductions in growth make climate change a small problem; smaller reductions in growth imply climate change is a very serious problem indeed. Analogously, reductions in the growth rate of population can be effective in controlling climate change.

This paper concerns computational models in environmental economics and policy, particularly so-called integrated assessment models. For the most part, such models are simply extensions of standard neoclassical growth models, extended by including the environment and pollution generation. We review the structure of integrated assessment models, distinguishing between finite horizon and infinite horizon models, both deterministic and stochastic. We present a new solution algorithm for infinite horizon integrated assessment models, relying on a neural net approximation of the value function within an iterative version of the Bellman equation.

The paper begins with the problem of a firm subject to random productivity shocks drawn from a particular distribution. We are concerned with the case whereby the distribution of the shocks changes without the knowledge of the firm. Over time the firm learns about the nature and extent of the change in the distribution of the shock and adjusts, incurring adjustment costs in the process. The long run loss in profits (Ž) due to the shift in the distribution we term the adaptation costs. The transitory profit loss, incurred while the firm is learning about the distribution shift, is termed the adjustment cost. The theory is developed and then applied to the problem of measuring adaptation and adjustment costs in the face of unanticipated and imperfectly observed climate change in agriculture. The empirical part of the paper involves estimating a supply function for corn that depends on actual weather realizations and expected weather, using county level data for the US. We then simulate the effect of an unobserved climate shock, where learning about the climate shock is by observing the weather and updating prior knowledge using Bayes Rule.