Small area estimation of the homeless in Los Angeles: An application of cost-sensitive stochastic gradient boosting

In many metropolitan areas efforts are made to count the homeless to ensure proper provision of social services. Some areas are very large, which makes spatial sampling a viable alternative to an enumeration of the entire terrain. Counts are observed in sampled regions but must be imputed in unvisited areas. Along with the imputation process, the costs of underestimating and overestimating may be different. For example, if precise estimation in areas with large homeless c ounts is critical, then underestimation should be penalized more than overestimation in the loss function. We analyze data from the 2004--2005 Los Angeles County homeless study using an augmentation of $L_1$ stochastic gradient boosting that can weight overestimates and underestimates asymmetrically. We discuss our choice to utilize stochastic gradient boosting over other function estimation procedures. In-sample fitted and out-of-sample imputed values, as well as relationships between the response and predictors, are analyzed for various cost functions. Practical usage and policy implications of these results are discussed briefly.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS328 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Rational Model of the Closed-End Fund Discount

The discount on closed-end funds is widely accepted as proof of investor irrationality. We show,however, that a parsimonious rational model can generate a discount that exhibits many of the characteristics observed in practice. The only required features of the model are that managers have (imperfectly observable) ability to generate excess returns; they sign long-term contracts guaranteeing them a fee each year equal to a fixed fraction of assets under management; and they can leave to earn more money elsewhere if they turn out to be good. With these assumptions, time-varying discounts are not an anomaly in a rational world with competitive investors -- they are required.

Mutual Fund Flows and Performance in Rational Markets

We develop a simple rational model of active portfolio management that provides a natural benchmark against which to evaluate observed relationship between returns and fund flows. We show that many effects widely regarded as anomalous are consistent with this simple explanation. In the model, investments with active managers do not outperform passive benchmarks because of the competitive market for capital provision, combined with decreasing returns to scale in active portfolio management. Consequently, past performance cannot be used to predict future returns, or to infer the average skill level of active managers. The lack of persistence in active manager returns does not imply that differential ability across managers is nonexistent or unrewarded, that gathering information about performance is socially wasteful, or that chasing performance is pointless. A strong relationship between past performance and the ow of funds exists in our model, indeed this is the market mechanism that ensures that no predictability in performance exists. Calibrating the model to the fund flows and survivorship rates, we nd these features of the data are consistent with the vast majority (80%) of active managers having at least enough skill to make back their fees.

Human Capital, Bankruptcy and Capital Structure

We derive a firm's optimal capital structure and managerial compensation contract when employees are averse to bearing their own human capital risk, while equity holders can diversify this risk away. In the presence of corporate taxes, our model delivers optimal debt levels consistent with those observed in practice. It also makes a number of predictions for the cross-sectional distribution of firm leverage. Consistent with existing empirical evidence, it implies persistent idiosyncratic differences in leverage across firms. An important new empirical prediction of the model is that, ceteris paribus, firms with more leverage should pay higher wages.

Past trauma and future choices: Differences in discounting in low-income, urban African Americans

AbstractBackgroundExposure to traumatic events is surprisingly common, yet little is known about its effect on decision making beyond the fact that those with post-traumatic stress disorder are more likely to have substance-abuse problems. We examined the effects of exposure to severe trauma on decision making in low-income, urban African Americans, a group especially likely to have had such traumatic experiences.MethodParticipants completed three decision-making tasks that assessed the subjective value of delayed monetary rewards and payments and of probabilistic rewards. Trauma-exposed cases and controls were propensity-matched on demographic measures, treatment for psychological problems, and substance dependence.ResultsTrauma-exposed cases discounted the value of delayed rewards and delayed payments, but not probabilistic rewards, more steeply than controls. Surprisingly, given previous findings that suggested women are more affected by trauma when female and male participants’ data were analyzed separately, only the male cases showed steeper delay discounting. Compared with nonalcoholic males who were not exposed to trauma, both severe trauma and alcohol-dependence produced significantly steeper discounting of delayed rewards.ConclusionsThe current study shows that exposure to severe trauma selectively affects fundamental decision-making processes. Only males were affected, and effects were observed only on discounting delayed outcomes (i.e. intertemporal choice) and not on discounting probabilistic outcomes (i.e. risky choice). These findings are the first to show significant differences in the effects of trauma on men's and women's decision making, and the selectivity of these effects has potentially important implications for treatment and also provides clues as to underlying mechanisms.</jats:sec

Counting the homeless in Los Angeles County

Over the past two decades, a variety of methods have been used to count the homeless in large metropolitan areas. In this paper, we report on an effort to count the homeless in Los Angeles County, one that employed the sampling of census tracts. A number of complications are discussed, including\^{E} the need to impute homeless counts to areas of \^{E}the County\^{E} not sampled. We conclude that, despite their imperfections, estimated counts provided useful and credible information to the stakeholders involved.Comment: Published in at http://dx.doi.org/10.1214/193940307000000428 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org