Estimation of Some Nonlinear Panel Data Models With Both Time-Varying and Time-Invariant Explanatory Variables

The so-called “fixed effects” approach to the estimation of panel data models suffers from the limitation that it is not possible to estimate the coefficients on explanatory variables that are time-invariant. This is in contrast to a “random effects” approach, which achieves this by making much stronger assumptions on the relationship between the explanatory variables and the individual-specific effect. In a linear model, it is possible to obtain the best of both worlds by making random effects-type assumptions on the time-invariant explanatory variables while maintaining the flexibility of a fixed effects approach when it comes to the time-varying covariates. This article attempts to do the same for some popular nonlinear models.     

Racial Discrimination in the U.S. Housing and Mortgage Lending Markets: A Quantitative Review of Trends, 1976–2016

We examine trends in racial and ethnic discrimination in U.S. housing and mortgage lending markets through a quantitative review of studies. We code and analyze as a time series results from 16 field experiments of housing discrimination and 19 observational studies of mortgage lending discrimination. Consistent with prior research, we find evidence of a decline in housing discrimination from the late 1970s to the present. Our results show that this trend holds in both the national audits sponsored by the U.S. Department of Housing and Urban Development (HUD) and in non-HUD studies. The decline in discrimination is strongest for discrimination that involves direct denial of housing availability, for which discrimination has declined to low levels. The downward trend in discrimination is weaker for measures reflecting the number of units recommended and inspected, and significant discrimination remains for these outcomes. In the mortgage market, we find that racial gaps in loan denial have declined only slightly, and racial gaps in mortgage cost have not declined at all, suggesting persistent racial discrimination. We discuss the implications of these trends for housing inequality, racial segregation, and racial disparities in household wealth.

     

Spatial heterogeneity in drinking water sources in the Greater Accra Metropolitan Area (GAMA), Ghana

Population and Environment - Universal access to safe drinking water is essential to population health and well-being, as recognized in the Sustainable Development Goals (SDG). To develop targeted...     

Estimation of tobit-type models with individual specific effects

The aim of this paper is two-fold. First, we review recent estimators for censored regression and sample selection panel data models with unobservable individual specific effects, and show how the idea behind these estimators can be used to construct estimators for a variety of other Tobit-type models. The estimators presented in this paper are semiparametric, in the sense that they do not require the parametrization of the distribution of the unobservables. The second aim of the paper is to introduce a new class of estimators for the censored regression model. The advantage of the new estimators is that they can be applied under a stationarity assumption on the transitory error terms, which is weaker than the exchangeability assumption that is usually made in this literature. A similar generalization does not seem feasible for the estimators of the other models that are considered.