Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models

In this paper we propose a test for the significance of categorical predictors in nonparametric regression models. The test is fully data-driven and employs cross-validated smoothing parameter selection while the null distribution of the test is obtained via bootstrapping. The proposed approach allows applied researchers to test hypotheses concerning categorical variables in a fully nonparametric and robust framework, thereby deflecting potential criticism that a particular finding is driven by an arbitrary parametric specification. Simulations reveal that the test performs well, having significantly better power than a conventional frequency-based nonparametric test. The test is applied to determine whether OECD and non-OECD countries follow the same growth rate model or not. Our test suggests that OECD and non-OECD countries follow different growth rate models, while the tests based on a popular parametric specification and the conventional frequency-based nonparametric estimation method fail to detect any significant difference.     

Why the Infinite Decision Puzzle is Puzzling

Pulier (2000, Theory and Decision 49: 291) and Machina (2000, Theory and Decision 49: 293) seek to dissolve the Barrett–Arntzenius infinite decision puzzle (1999, Theory and Decision 46: 101). The proposed dissolutions, however, are based on misunderstandings concerning how the puzzle works and the nature of supertasks more generally. We will describe the puzzle in a simplified form, address the recent misunderstandings, and describe possible morals for decision theory.     

Applying State Space to SPC: Monitoring Multivariate Time Series

Monitoring cross-sectional and serially interdependent processes has become a new issue in statistical process control (SPC). In up-to-date SPC literature, Kalman filtering was reported to monitor univariate autocorrelated processes. This paper applies a Kalman filter or state-space method for SPC to monitoring multivariate time series. We use Aoki's approach to estimate the parameter matrices of a state-space model. Multivariate Hotelling T ² control charts are employed to monitor the residuals of the state-space. Examples of this approach are illustrated.     

ALTRUISM, MATCHING, AND NONMARKET INSURANCE

Incomplete market insurance gives rise to nonmarket arrangements for coinsurance. We find that the effort altruistically linked individuals take to avoid an accident increases with the degree of altruism. If the degree of altruism is sufficiently high, an economy with nonmarket insurance yields higher social welfare than an economy without nonmarket insurance. As altruism increases, the equilibrium level of effort approaches the second-best solution without the need for costly monitoring. Coinsurance is above (below) the socially optimal level if individuals place greater weight (less weight) on their own utility than on that of their partners.     

A Social Choice Lemma on Voting Over Lotteries with Applications to a Class of Dynamic Games

We prove a lemma characterizing majority preferences over lotteries on a subset of Euclidean space. Assuming voters have quadratic von Neumann–Morgenstern utility representations, and assuming existence of a majority undominated (or “core”) point, the core voter is decisive: one lottery is majority-preferred to another if and only if this is the preference of the core voter. Several applications of this result to dynamic voting games are discussed.This paper was completed after Jeff Banks’s death. John Duggan is deeply indebted to him for his friendship and his collaboration on this and many other projects.     

The Obese Smoker's Wage Penalty*

Objective. Smoking and obesity are associated with significant wage penalties when considered separately. We assess the combined effects of those behaviors. Methods. We estimate the effects of smoking and obesity on wages using multivariate regression analysis with 1979 National Longitudinal Survey of Youth data. Results. The raw data indicate that obese smokers experience large earnings penalties. However, these penalties are not found to be interactive or directly causal for most of the demographic subgroups we examine. One exception is the wage penalty associated with obesity for females, which remains significant throughout the analysis. Conclusions. In the absence of a demonstrable direct causal effect of those behaviors on wages, associated individual‐specific socioeconomic factors appear to be the driving forces behind the obese smoker wage penalty. Not included, but potentially significant, are the effects of employer and customer discrimination against obese smokers and the possible labor policy implications of such discrimination.     

A Small-Sample Estimator for the Sample-Selection Model

A semiparametric estimator for evaluating the parameters of data generated under a sample selection process is developed. This estimator is based on the generalized maximum entropy estimator and performs well for small and ill-posed samples. Theoretical and sampling comparisons with parametric and semiparametric estimators are given. This method and standard ones are applied to three small-sample empirical applications of the wage-participation model for female teenage heads of households, immigrants, and Native Americans.