We develop a theory that rationalizes the use of a dominant unit of account in an economy. Agents enter into non-contingent contracts with a variety of business partners. Trade unfolds sequentially in credit chains and is subject to random matching. By using a dominant unit of account, agents can lower their exposure to relative price risk, avoid costly default, and create more total surplus. We discuss conditions under which it is optimal to adopt circulating government paper as the dominant unit of account, and the optimal choice of ?currency areas? when there is variation in the intensity of trade within and across regions.

This paper provides a model of boom-bust episodes in middle-income countries. It is based on sectoral differences in corporate finance: the nontradables sector is special in that it faces a contract enforceability problem and enjoys bailout guarantees. As a result, currency mismatch and borrowing constraints arise endogenously in that sector. This sectoral asymmetry allows the model to replicate the main features of observed boom-bust episodes. In particular, episodes begin with a lending boom and a real appreciation, peak in a self-fulfilling crisis during which a real depreciation coincides with widespread bankruptcies, and end in a recession and credit crunch. The nontradables sector accounts for most of the volatility in output and credit.

This paper considers a consumption-based asset pricing model where housing is explicitly modeled both as an asset and as a consumption good. Nonseparable preferences describe households’ concern with composition risk, that is, fluctuations in the relative share of housing in their consumption basket. Since the housing share moves slowly, a concern with composition risk induces low frequency movements in stock prices that are not driven by news about cash flow. Moreover, the model predicts that the housing share can be used to forecast excess returns on stocks. We document that this indeed true in the data. The presence of composition risk also implies that the riskless rate is low which further helps the model improve on the standard CCAPM.

Recent events have stimulated interest in the joint behavior of prices and quantities in credit markets. Data sources such as the Federal Reserve Board’s Flow of Funds Accounts (FFA) provide statistics on a rich set of credit market instruments. However, it is challenging to inter pret such data using economic models that speak to the allocation of risk across agents, such as households or intermediaries. On the one hand, an instrument class such as “Treasury bonds” typically contains many dif ferent instruments that trade at different prices and have different exposure to interest rate shocks (for example, because of differences in duration). On the other hand, a lot of the price movements in instruments like Treasury bonds and mortgage backed securities are due to com mon interest rate shocks, making those instru ments close substitutes from a portfolio choice perspective. For understanding how interest rate risk is allocated in the economy, one would thus like to use information on many positions at the same time, rather than, say, focus on one set of instruments only. At the same time, models with many closely substitutable assets are problem atic. Instead, it would be desirable to compress position data into simple sets of portfolios, like “long” and “short” bonds, but with some con fidence that the risk properties of the original instruments are not lost along the way. DemanD anD Supply for Government BonDS

This paper studies household beliefs dur ing the recent US housing boom. The first part presents evidence from the Michigan Survey of Consumers. To characterize the heterogeneity in households’ views about housing and the econ omy, we perform a cluster analysis on survey responses at different stages of the boom. The estimation always finds a small cluster of house holds that believe it is a good time to buy a house because house prices will rise further. The size of this “momentum” cluster strongly increased toward the end of the boom. The second part of the paper provides a simple search model of the housing market to show how a small number of optimistic investors can have a large effect on prices without buying a large share of the housing stock. The raw survey data suggest that the housing boom had two distinct phases. During the early boom years 2002–2003, a large and increasing fraction of households believed that the time for buying a house was good. This fraction peaked at 85.2 percent in 2003:II. The most important reason—cited by up to 72 percent of house

This paper studies a New Keynesian business cycle model with agents who are averse to ambiguity (Knightian uncertainty). Shocks to confidence about future TFP are modeled as changes in ambiguity. To assess the size of those shocks, our estimation uses not only data on standard macro variables, but also incorporates the dispersion of survey forecasts about growth as a measure of confidence. Our main result is that TFP and confidence shocks together can explain roughly two thirds of business cycle frequency movements in the major macro aggregates. Confidence shocks account for about 70 percent of this variation.

This paper studies how managers plan under uncertainty. In a new panel survey of German manufacturing firms, we observe both forecasts of sales growth and a quantitative measure of subjective uncertainty. We show that subjective uncertainty reflects change experienced by firms: It is high when growth is either unusually low or unusually high. Subjective uncertainty is more than conditional volatility; while more volatile firms are typically more uncertain, experienced change alone increases uncertainty, consistent with a model of learning. Uncertainty over our sample is mostly idiosyncratic but varies substantially over time and leads managers to plan lower employment and prices.

This paper uses an assignment model to understand the cross section of house prices within a metro area. Movers’ demand for housing is derived from a life-cycle problem with credit market frictions. Equilibrium house prices adjust to assign houses that differ by quality to movers who differ by age, income, and wealth. To quantify the model, we measure distributions of house prices, house qualities, and mover characteristics from micro-data on San Diego County during the 2000s boom. The main result is that cheaper credit for poor households was a major driver of prices, especially at the low end of the market.

This paper studies housing markets where a subset of houses in a restricted area is available exclusively to a subset of “eligible” buyers. An empirical part shows that houses on Stanford campus (available only to faculty) trade at substantial discounts to comparable houses off campus. The theoretical part describes an assignment model with heterogeneous houses and buyers which predicts such discounts if the matchup of quality and buyer pools is sufficiently different inside versus outside the restricted area. The restriction can distort allocations by making eligible buyers choose either higher or lower qualities than ineligible buyers with the same characteristics.

This paper considers learning when the distinction between risk and ambiguity matters. It first describes thought experiments, dynamic variants of those provided by Ellsberg, that highlight a sense in which the Bayesian learning model is extreme—it models agents who are implausibly ambitious about what they can learn in complicated environments. The paper then provides a generalization of the Bayesian model that accommodates the intuitive choices in the thought experiments. In particular, the model allows decision-makers' confidence about the environment to change—along with beliefs—as they learn. A portfolio choice application compares the effect of changes in confidence under ambiguity vs. changes in estimation risk under Bayesian learning. The former is shown to induce a trend towards more stock market participation and investment even when the latter does not.