Count data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum...

Spatial connectivity of renewable resources induces a spatial externality in extraction. We explore the consequences of decentralized spatial property rights in the presence of spatial externalities. We generalize the notion of unitization - developed to enhance cooperative extraction of oil and...

We build a novel stochastic dynamic regional integrated assessment model (IAM) of the climate and economic system including a number of important climate science elements that are missing in most IAMs. These elements are spatial heat transport from the Equator to the Poles, sea level rise,...

Geography shapes economic outcomes in a major way. This paper uses spatial empirical methods to detect and analyze trade patterns in a historical data set on Chinese rice prices. Our results suggest that spatial features were important for the expansion of interregional trade. Geography...

We provide a new and superior measure of U.S. GDP, obtained by applying optimal signal-extraction techniques to the (noisy) expenditure-side and income-side estimates. Its properties - particularly as regards serial correlation - differ markedly from those of the standard expenditure-side...

In this paper I analyze the relationships among investment, q, and cash flow in a tractable stochastic model in which marginal q and average q are identically equal. After analyzing the impact of changes in the distribution of the marginal operating profit of capital, I extend the model to...

In three sets of experiments involving over 4,200 subjects, we show that agents motivated to be selfish make systematic decision errors of the kind generally attributed to cognitive limitations or behavioral biases. We show that these decision errors are eliminated (or dramatically reduced) when...

We propose point forecast accuracy measures based directly on distance of the forecast-error c.d.f. from the unit step function at 0 ("stochastic error distance," or SED). We provide a precise characterization of the relationship between SED and standard predictive loss functions, and we show...

It is common in empirical research to use what appear to be sensible rules of thumb for cleaning data. Measurement error is often the justification for removing (trimming) or recoding (winsorizing) observations whose values lie outside a specified range. This paper considers identification in a...

We propose a general method of moments technique to identify measurement error in self-reported and transcript-reported schooling using differences in wages, test scores, and other covariates to discern the relative verity of each measure. We also explore the implications of such reporting...