A simple asset pricing model with both endogenous stock market participation and subjective risk can explain the negative cross-country correlation between participation rates and the volatility of excess returns, along with the time-varying participation rates in the data. Belief-driven learning dynamics can explain the interplay between participation rates, subjective risk, and stock price volatility. When agents adaptively learn about the risk and return, my model generates 25% of the excess volatility observed in US stock prices, while also matching key moments. With learning about risk, excess volatility of stock prices is driven by fluctuations in the participation rate that arise because agents’ risk estimates vary with prices. I find that learning about risk is quantitatively more important than learning about returns.

In an incomplete markets economy with sunspots, the Pareto-criterion cannot rank sunspot equilibria of different levels of excess price-level volatility. Therefore, I propose a measure of excess volatility cost in terms of a period-0 endowment good. Ex-ante endowment subsidies are provided, in theory, to each consumer, so that the resulting equilibrium allocation of the higher volatility is Pareto-equivalent to the original benchmark equilibrium with a lower volatility level. The aggregate volatility cost is computed as the sum of all consumers' subsidies. Focusing on local analysis that considers small variations around a given volatility level, I show that the aggregate cost strictly increases in volatility even though each individual cost does not necessarily have this property.

This paper provides a user-friendly approach to explain how variation in fundamental price-determining variables ‘translates into’ variation in the fundamental value of equities, based on the standard dividend-growth model. The analysis is illustrated with UK data using estimates of real interest rate forecasts and real dividend growth rate forecasts in the past. An important application of this approach is that stock market volatility can be analysed in terms of its component parts. Actual market volatility does not appear to be excessive when compared with the notional volatility implied by changes over time in our estimates of forecast real interest rates and forecast real dividend growth rates.

We study how the use of judgment or “add-factors” in forecasting may disturb the set of equilibrium outcomes when agents learn by using recursive methods. We isolate conditions under which new phenomena, which we call exuberance equilibria, can exist in a standard self-referential environment. Local indeterminacy is not a requirement for existence. We construct a simple asset-pricing example and find that exuberance equilibria, when they exist, can be extremely volatile relative to fundamental equilibria.

Different theories of expectation formation and learning usually yield different outcomes for realized market prices in dynamic models. The purpose of this paper is to investigate expectation formation and learning in a controlled experimental environment. Subjects are asked to predict the next period's aggregate price in a dynamic commodity market model with feedback from individual expectations. Subjects have no information about underlying market equilibrium equations, but can learn by observing past price realizations and predictions. We conduct a stable, an unstable, and a strongly unstable treatment. In the stable treatment, rational expectations (RE) yield a good description of observed aggregate price fluctuations: prices remain close to the RE steady state. In the unstable treatments, prices exhibit large fluctuations around the RE steady state. Although the sample mean of realized prices is close to the RE steady state, the amplitude of the price fluctuations as measured by the variance is significantly larger than the amplitude under RE, implying persistent excess volatility. However, agents' forecasts are boundedly rational in the sense that fluctuations in aggregate prices are unpredictable and exhibit no forecastable structure that could easily be exploited.

Different theories of expectation formation and learning usually yield different outcomes for realized market prices in dynamic models. The purpose of this paper is to investigate expectation formation and learning in a controlled experimental environment. Subjects are asked to predict the next period's aggregate price in a dynamic commodity market model with feedback from individual expectations. Subjects have no information about underlying market equilibrium equations, but can learn by observing past price realizations and predictions. We conduct a stable, an unstable, and a strongly unstable treatment. In the stable treatment, rational expectations (RE) yield a good description of observed aggregate price fluctuations: prices remain close to the RE steady state. In the unstable treatments, prices exhibit large fluctuations around the RE steady state. Although the sample mean of realized prices is close to the RE steady state, the amplitude of the price fluctuations as measured by the variance is significantly larger than the amplitude under RE, implying persistent excess volatility. However, agents' forecasts are boundedly rational in the sense that fluctuations in aggregate prices are unpredictable and exhibit no forecastable structure that could easily be exploited.

A recent article in The Economist magazine divided economists into “poets” and “plumbers,” the former articulating radical new visions of the field and the latter patiently installing the infrastructure needed to implement those visions. Bob Shiller is the rare economist who is both poet and plumber. Not only that, he is also entrepreneur and pundit. His work has fundamentally changed the theory, econometrics, practice, and popular understanding of finance.

Starting in the late 1970's, Bob boldly challenged the prevailing orthodoxy of financial economics. He showed that financial asset prices often deviate substantially from the levels predicted by simple efficient-markets models, and he developed new empirical methods to measure these price deviations. In the early 1980's, Bob went on to argue that economists need a much more detailed understanding of investor psychology if they are to understand asset price movements. He pioneered the emerging field of behavioral economics and its most successful branch, behavioral finance. At the end of the century, Bob articulated his vision of finance in a wildly successful popular book, Irrational Exuberance. He became so well known that TIAA-CREF asked him to appear in a series of full-page advertisements in the popular press.

Although Bob does not believe that investors use financial markets in a perfectly rational manner, he does believe that these markets offer great possibilities to improve the human condition. His recent work asks how existing financial markets can be used, and new financial markets can be designed, to improve the sharing of risks across groups of people in different regions, countries, and occupations. He has explored risk-sharing possibilities not only in journal articles, but also in business ventures and a 2003 book, The New Financial Order: Risk in the 21st Century.

It was a great privilege for me to interview Bob Shiller. Bob's arrival at Yale when I was a Ph.D. student there set the course of my career as an economist. Bob reinvigorated the Yale tradition of macroeconomics, with its emphasis on the central role of financial markets in the macroeconomy and its idealism about the possibility of improving macroeconomic outcomes. First as a thesis adviser, then as a coauthor, mentor, and friend, Bob showed me how to contribute to this tradition.

The interview took place at the 2003 annual meetings of the Allied Social Science Associations in Washington, D.C. We met in a hotel suite, ate a room service meal, and had the enjoyable conversation that is reproduced below.

We introduce adaptive learning behavior into a general-equilibrium life-cycle economy with capital accumulation. Agents form forecasts of the rate of return to capital assets using least-squares autoregressions on past data. We show that, in contrast to the perfect-foresight dynamics, the dynamical system under learning possesses equilibria that are characterized by persistent excess volatility in returns to capital. We explore a quantitative case for theselearning equilibria. We use an evolutionary search algorithm to calibrate a version of the system under learning and show that this system can generate data that matches some features of the time-series data for U.S. stock returns and per-capita consumption. We argue that this finding provides support for the hypothesis that the observed excess volatility of asset returns can be explained by changes in investor expectations against a background of relatively small changes in fundamental factors.