We develop a framework to measure the welfare impact of macroeconomic shocks throughout the distribution. The first-order impact of a shock is summarized by the induced movements in agents' feasible sets: their budget constraint and borrowing constraints. We combine estimated impulse response functions with micro-data on household consumption bundles, asset holdings, and labor income for different US households. We find that inflationary oil shocks are regressive, but monetary expansions are progressive, and there is substantial heterogeneity throughout the life cycle. In all cases, the dominant channel is the effect of the shock on the cost of accumulating assets, not movements in goods prices or labor income.

Portfolio selection problems have been thoroughly studied under the risk-and-return paradigm introduced by Markowitz. However, the usefulness of this approach has been hindered by some practical considerations that have resulted in poorly diversified portfolios, or, solutions that are extremely sensitive to parameter estimation errors. In this work, we use sampling methods to cope with this issue and compare the merits of two approaches: a sample average approximation approach and a performance-based regularization (PBR) method that appeared recently in the literature. We extend PBR by incorporating three different risk metrics—integrated chance-constraints, quantile deviation, and absolute semi-deviation—and deriving the corresponding regularization formulas. Additionally, a numerical comparison using index-based portfolios is presented using historic data that includes the subprime crisis.

Aggregation is a tool used to reduce the complexity of economic models in order to draw more clear and succinct conclusions or simplify analyses. As any approximation, its use may be accompanied with errors researchers may not be willing to tolerate if they become aware of them. In this work I present how these errors appear using aggregation across goods and across consumers. To this end, I consider aggregation as a means to approximate probability distributions over parameters. Using this approach, I show ways to bound approximation errors by tailoring the parameters of the model. Further, I briefly discuss a methodology to study the goodness-of-fit of aggregate models in more general settings.