This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.

This paper investigates a firm's stock return asynchronicity through the auditor's perspective to distinguish whether this asynchronicity can proxy for the company's firm-specific information or the quality of its information environment. We find a significant and positive association between asynchronicity and audit fees after controlling for auditor quality and other factors that affect audit fees, suggesting that stock return asynchronicity is more likely to capture a company's firm-specific information than its information environment. We also find that asynchronous firms are more likely to receive adverse opinions on their internal controls over financial reporting, but are associated with lower costs of capital and auditor litigation, providing further evidence in support of the firm-specific information argument. Asynchronicity's positive association with audit fees is driven by firms with higher accounting reporting complexity, suggesting stock return asynchronicity captures a firm's complexity, resulting in more significant efforts by the auditor.

Understanding human behavior in its entire complexity is an ambitious if not impossible challenge. It is however possible to study particular aspects of human behavior through experiments that allow us to isolate specific facets in the decision-making process, ultimately leading to a better understanding of human behavior as a whole. This thesis covers three experimental articles on unethical economic behavior and sheds light on the motives and circumstances that lead individuals to engage in these activities. Clearly, unethical behavior in all its different manifestations can pose great risk to society – both at the large (e.g. corporate tax evasion) and small (e.g. shoplifting) scale – making it a relevant topic to be studied in economic research. Trying to understand unethical behavior through the lenses of traditional economic theory is problematic.