Multinational Firms and the Factor Intensity of Trade

In studying the impact of direct investment on the amount, direction, and composition of international trade we have found that the multinational firm fits uncomfortably into the usual theory of trade and capital movements. We attempt here to introduce the fact of the existence of multinational firms into the explanation of trade flows and particularly into the long-running debate over the relations among factor abundance, factor prices and trade.

Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth-death processes

Surveys often ask respondents to report nonnegative counts, but respondents may misremember or round to a nearby multiple of 5 or 10. This phenomenon is called heaping, and the error inherent in heaped self-reported numbers can bias estimation. Heaped data may be collected cross-sectionally or longitudinally and there may be covariates that complicate the inferential task. Heaping is a well-known issue in many survey settings, and inference for heaped data is an important statistical problem. We propose a novel reporting distribution whose underlying parameters are readily interpretable as rates of misremembering and rounding. The process accommodates a variety of heaping grids and allows for quasi-heaping to values nearly but not equal to heaping multiples. We present a Bayesian hierarchical model for longitudinal samples with covariates to infer both the unobserved true distribution of counts and the parameters that control the heaping process. Finally, we apply our methods to longitudinal self-reported counts of sex partners in a study of high-risk behavior in HIV-positive youth.Comment: Published at http://dx.doi.org/10.1214/15-AOAS809 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Mortgage Default Risk and Real Estate Prices: The Use of Index-Based Futures and Options in Real Estate

Evidence is shown, using US foreclosure data by state 1975-93, that periods of high default rates on home mortgages strongly tend to follow real estate price declines or interruptions in real estate price increase. The relation between price decline and foreclosure rates is modelled using a distributed lag. Using this model, holders of residential mortgage portfolios could hedge some of the risk of default by taking positions in futures or options markets for residential real estate prices, were such markets to be established.

A Bayesian Dirichlet Auto-Regressive Moving Average Model for Forecasting Lead Times

Lead time data is compositional data found frequently in the hospitality industry. Hospitality businesses earn fees each day, however these fees cannot be recognized until later. For business purposes, it is important to understand and forecast the distribution of future fees for the allocation of resources, for business planning, and for staffing. Motivated by 5 years of daily fees data, we propose a new class of Bayesian time series models, a Bayesian Dirichlet Auto-Regressive Moving Average (B-DARMA) model for compositional time series, modeling the proportion of future fees that will be recognized in 11 consecutive 30 day windows and 1 last consecutive 35 day window. Each day's compositional datum is modeled as Dirichlet distributed given the mean and a scale parameter. The mean is modeled with a Vector Autoregressive Moving Average process after transforming with an additive log ratio link function and depends on previous compositional data, previous compositional parameters and daily covariates. The B-DARMA model offers solutions to data analyses of large compositional vectors and short or long time series, offers efficiency gains through choice of priors, provides interpretable parameters for inference, and makes reasonable forecasts