The objective of this thesis is to explore the implications of learning as an alternative expectations formation mechanism in international macroeconomics. The first part of the thesis (Chapters 2 to 4) deals with the effects of adaptive learning (Evans and Honkapohja, 2001) in the transmission of policy changes and shocks within and across borders. In particular, learning has been introduced to two major workhorse open economy models: the Mundell-Fleming Dornbusch and the McKibbin-Sachs Global (MSG3) models. The second part of the thesis (Chapters 5 to 6) begins by examining the learning behavior of international creditors about the credibility of an exchange rate regime using a game theoretic approach to reputation. Using the Pooled Mean Group methodology, this part also conducts an empirical analysis to investigate the inter-relationship between sovereign yield spreads and exchange rate regime during the pre-World War One era...

The dissertation is composed of three chapters that contribute to the areas of macroeconomics and econometrics. All chapters utilize computer-based simulation methods. Chapter one, "Percentile and Percentile-t Bootstrap Confidence Intervals: A Practical Comparison", employs a Monte Carlo study to compare the performance of bootstrap percentile-t, bootstrap percentile, and standard asymptotic confidence intervals in two distinct heteroscedastic regression models. Results are consistent across models but different among bootstrap algorithms employed, in that for the XY algorithm all three methods perform similarly, but for the wild algorithm the percentile-t significantly outperforms the other methods. The implications are that for the models considered in this study, practitioners who choose to utilize these types of bootstrap confidence intervals should opt for the percentile-t method coupled with the wild bootstrap algorithm. Chapter two, "Asset Pricing with Active Traders and Passive Investors: An Adaptive Learning Approach", constructs a model of active traders and passive investors in production and endowment based stochastic growth asset pricing frameworks. The model replaces rational expectations with an adaptive learning rule that forecasts future equity prices with econometric methods. The paper estimates the model and compares the results to U.S. economic and financial stylized facts. Numerical results show that the model of active traders and passive investors matches several of the stylized facts better than does a model that assumes a fully rational, representative agent. Chapter three, "A Simple Exchange Rate Model with Heterogeneous Agents", employs a simple monetary framework to construct a heterogeneous agent model of flexible exchange rates. The model replaces rational expectations with an adaptive learning rule that forecasts future exchange rates with econometric methods. The paper estimates the model and compares the results to foreign exchange market stylized facts. Numerical results show that the model matches several of the stylized facts better than does a model that assumes a fully rational, representative agent.

This dissertation consists of three essays that examine various problems in financial economics. Chapter 1 fills in a gap in the IPO literature by documenting a close connection between IPO underpricing and the long-term underperformance of IPOs. Firms going public in periods of low underpricing do not underperform in the long run, while firms going public in high underpricing periods do. Furthermore, IPOs in later stages of high underpricing periods underperform even relative to their offer prices, which suggests that many of the most "underpriced" IPOs are in fact priced above fundamental value. This result is unlikely to be explained by differences in risk, or to be driven by a peso problem. I also find that firms going public in later stages of high underpricing periods display worse operating performance and profitability, lower asset growth, lower investment rates and higher cash holdings. Finally, I provide evidence that investor sentiment is stronger in high-underpricing periods. These results are consistent with a setting in which low quality firms, in periods in which the average underpricing in the market is high, try to exploit investors' sentiment by going public. Chapter 2 looks at the return predictability information in Single Country Closed-End Fund (SCCEF) discounts. It is long argued that discounts in closed-end funds are caused by differences in sentiment between investors that trade the fund and investors that trade the underlying assets. SCCEFs provide an interesting setting given the clear market segmentation. American SCCEFs are priced by American investors, while underlying assets are mainly traded by investors in the respective country. I argue that if cross-sectional and time-series variation in SCCEFs are linked to differences in sentiment, then the SCCEF discount can be used to predict future performance of SCCEFs, international stock markets, or both. The evidence on international stock markets' return predictability using SCCEF discounts is mixed. A trading strategy designed to exploit potential differences in sentiment by buying and selling international stock indices delivers alphas of around 90bps per month in an International CAPM. Adding three extra factors: value, size and momentum in U.S. equity does not change the result. However, once we control for international value and momentum in stock markets, we no longer observe positive alphas for short-horizon investments. The evidence on SCCEF return predictability from SCCEF discounts is very strong. For all three asset pricing models considered, a strategy that exploits differences in sentiment yields positive alphas, with magnitudes ranging from 2% to 4% per month. In Chapter 3, I investigate how the stock market reacts to earnings surprises announced during major sport events in the U.S. In a rational and frictionless market, investors should not react differently to announcements released during sport events. However, major sport events combine two known psychological biases. First, sports can be distracting, impairing investors' judgment. Second, sports can change people's mood. Hence, through these biases, market prices could be affected. Considering the Super Bowl, World Series of Baseball and NBA finals I find that investors, immediately after sport events, underreact to positive surprises, and overreact to negative surprises in earnings. After this initial reaction, I find that, investors undo their 'mistakes' in the following weeks to the announcement. However, for the most negative and positive surprises, they over-compensate. In this study, I show that non relevant financial events have an impact on market prices. Moreover, I show that the observed impact cannot be explained only by limited attention, as investor mood seems to be crucial to explain investors' reactions.