I study asset price amplification in an asymmetric information model. Entrepreneurs issue debt to finance investments in a physical asset. They have private information about their success probabilities. For a given debt level, higher asset prices require entrepreneurs to invest more of their own funds. This makes bad entrepreneurs more reluctant to mimic good ones; as a result, good entrepreneurs increase their equilibrium leverage and invest more, and this amplifies the initial asset price increase. This model generates predictions about the credit market that are qualitatively consistent with existing evidence. Received April 24, 2015; editorial decision June 15, 2016 by Editor Itay Goldstein.

I study asset price amplification in an asymmetric information model. Entrepreneurs issue debt to finance investments in a physical asset. They have private information about their success probabilities. For a given debt level, higher asset prices require entrepreneurs to invest more of their own funds. This makes bad entrepreneurs more reluctant to mimic good ones; as a result, good entrepreneurs increase their equilibrium leverage and invest more, and this amplifies the initial asset price increase. This model generates predictions about the credit market that are qualitatively consistent with existing evidence.

Structural estimation of matching games with transferable utility, including matching games of trading networks and many-to-many matching, is increasingly popular in empirical work. I explore several modeling decisions that need to be made when specifying a structural model for a matching game. One decision is the choice of a game theoretic solution concept to impose in the structural model. I discuss pairwise stability, competitive equilibrium, and noncooperative games such as auctions. Another decision is whether to work with a continuum of agents or a finite number of agents. I explore other issues as well.

Homeowners at risk of default face a debt overhang that reduces their incentive to invest in their property: in expectation, some value created by investments in the property will go to the lender. This agency conflict affects housing investments. Homeowners at risk of default cut back substantially on home improvements and mortgage principal payments, even when they appear financially unconstrained. Meanwhile, they do not reduce spending on assets that they may retain in default, including home appliances, furniture, and vehicles. These findings highlight an important financial friction that has stifled housing investment since the Great Recession.

Using unique data on Canadian households, we show that financial advisors exert substantial influence over their clients' asset allocation, but provide limited customization. Advisor fixed effects explain considerably more variation in portfolio risk and home bias than a broad set of investor attributes that includes risk tolerance, age, investment horizon, and financial sophistication. Advisor effects remain important even when controlling flexibly for unobserved heterogeneity through investor fixed effects. An advisor's own asset allocation strongly predicts the allocations chosen on clients' behalf. This one‐size‐fits‐all advice does not come cheap: advised portfolios cost 2.5% per year, or 1.5% more than life cycle funds.

Governments regularly subsidize new ventures to spur innovation. This paper conducts the first large-sample, quasi-experimental evaluation of R&D subsidies. I use data on ranked applicants to the US Department of Energy's SBIR grant program. An early-stage award approximately doubles the probability that a firm receives subsequent venture capital and has large, positive impacts on patenting and revenue. These effects are stronger for more financially constrained firms. Certification, where the award contains information about firm quality, likely does not explain the grant effect. Instead, the grants are useful because they fund technology prototyping.

We propose a novel approach to measuring firm-level risk exposures and costs of equity. Using a simple consumption-based asset pricing model that explains nearly two-thirds of the variation in average returns across 55 anomaly portfolios, we map the relation between exposures to consumption risk and portfolio-level characteristics. We use this relation to calculate exposures to consumption risk at the firm level and show that the calculated consumption risk exposures yield portfolios with large differences in average returns and ex post consumption risk exposures consistent with those predicted by our calculated betas. Further, industry betas and risk premia implied by our procedure display economically intuitive variation over time. Finally, Fama-MacBeth regressions suggest that risk exposures calculated using our procedure dominate those from alternative factor models at explaining cross-sectional variation in returns.