Monetary policy under financial uncertainty

Monetary policy may play a substantial role in mitigating the effects of financial crises. In this paper, I suppose that the economy occasionally but infrequently experiences crises, where financial variables affect the broader economy. I analyze optimal monetary policy under such financial uncertainty, where policymakers recognize the possibility of crises. Optimal monetary policy is affected during the crisis and in normal times, as policymakers guard against the possibility of crises. In the estimated model this effect is quite small. Optimal policy does change substantially during a crisis, but uncertainty about crises has relatively little effect.     

Utilizing the balanced scorecard for R&D performance measurement

R&D programs are critical for many firms to achieve and sustain competitive advantage. Yet, measuring R&D performance over time can be quite complex due to inherent uncertainty. The paper responds to calls in the R&D literature to explore integrated performance measurement systems that capture financial and nonfinancial performance. We integrate the Stage-Gate approach to R&D management with the Balanced Scorecard to present a framework to show how firms can link resource commitments to these activities and the firm's strategic objectives. In this paper, we provide specific examples of how firms can apply this integrated performance measurement system to the R&D function.     

Friends with bankruptcy protection benefits

We show information spillovers limit the effectiveness of targeted debt relief programs. We study individuals who learn about the likelihood of debt relief from the recent experiences of workplace peers filing for bankruptcy protection. Peers granted bankruptcy can discharge debts, while peers facing dismissal lose all protections. Exploiting the random assignment of judges to bankruptcy cases, we determine that individuals with a “dismissed peer” are significantly less likely to file for bankruptcy or enter foreclosure. We highlight a novel channel relating social networks to household finances and identify additional costs of granting individual debt relief imposed on lenders.     

GENERALIZED STOCHASTIC GRADIENT LEARNING*

We study the properties of the generalized stochastic gradient (GSG) learning in forward‐looking models. GSG algorithms are a natural and convenient way to model learning when agents allow for parameter drift or robustness to parameter uncertainty in their beliefs. The conditions for convergence of GSG learning to a rational expectations equilibrium are distinct from but related to the well‐known stability conditions for least squares learning.     

Accounting for time-dependent treatment use when developing a prognostic model from observational data: A review of methods

Failure to account for time-dependent treatment use when developing a prognostic model can result in biased future predictions. We reviewed currently available methods to account for treatment use when developing a prognostic model. First, we defined the estimands targeted by each method and examined their mechanisms of action with directed acyclic graphs (DAGs). Next, methods were implemented in data from 1,906 patients; 325 received selective β-blockers (SBBs) during follow-up. We demonstrated seven Cox regression modeling strategies: (a) ignoring SBB treatment; (b) excluding SBB users or (c) censoring them when treated; (d) inverse probability of treatment weighting after censoring (IPCW), including SBB treatment as (e) a binary or (f) a time-dependent covariate; and (g) marginal structural modeling (MSM). Using DAGs, we demonstrated IPCW and MSM have the best properties and target a similar estimand. In the case study, compared to (a), approaches (b) and (e) provided predictions that were 1% and 2% higher on average. Performance (c-statistic, Brier score, calibration slope) varied minimally between approaches. Our review of methods confirmed that ignoring treatment is theoretically inferior, but differences between the prediction models obtained using different methods can be modest in practice. Future simulation studies and applications are needed to assess the value of applying IPCW or MSM to adjust for treatments in different treatment and disease settings.     

Informal Housing in the United States

Research on informal housing tends to focus overwhelmingly on less developed countries, downplaying or ignoring entirely the presence of informality in United States housing markets. In actuality, a longstanding and widespread tradition of informal housing exists in the United States but is typically disregarded by scholars. In this article we draw on three definitions of informality—as non‐compliant, non‐enforced, or deregulated economic activity—to characterize examples of informality in US housing markets, focusing in particular on five institutions that govern housing market activity in this country: property rights law, property transfer law, land‐use and zoning, subdivision regulations, and building codes. The cases presented here challenge the notion that informality is absent from US housing markets and highlight the unique nature of informal housing, US style—namely, that informal housing in the US is geographically uneven, largely hidden and typically interwoven within formal markets. We conclude with a discussion of how research on informal housing in the US can inform research in the global South.