Nonparametric estimation and hypothesis testing in econometric models

In this paper we systematically review and develop nonparametric estimation and testing techniques in the context of econometric models. The results are discussed under the settings of regression model and kernel estimation, although as indicated in the paper these results can go through for other econometric models and for the nearest neighbor estimation. A nontechnical survey of the asymptotic properties of kernel regression estimation is also presented. The technique described in the paper are useful for the empirical analysis of the economic relations whose true functional forms are usually unknown.     

The econometric analysis of models with risk terms

In this paper we have attempted to provide an integrated approach to the estimation of models with risk terms. It was argued that there exist orthogonality conditions between variables in the information set and higher-order moments of the unanticipated variable density. These could be exploited to provide consistent estimators of the parameters associated with the risk term. Specifically, it was recommended that an IV estimator should be applied, with instruments constructed from the information set. Four existing methods commonly used to estimate models with risk terms are examined, and applications of the techniques are made to the estimation of the risk term in the $US/$C exchange market, and the effects of price uncertainty upon production.     

Risk,trust, and altruism in genetic data sharing

How does concern about genetic data privacy compare with other concerns? We conduct behavioral experiments to compare risk attitudes towards sharing genetic data with a healthcare provider with risk attitudes towards sharing financial data with a money manager. Both scenarios involve identical decisions and monetary stakes, permitting us to focus on how the framing of data sharing influences attitudes. To delve deeper into individual motivations to share data, we provide treatments that study how data sharers' altruism and trust affect their decisions. Our findings (with 162 subjects) indicate that individuals are more willing to risk a loss to privacy of genetic data (for an anticipated return framed as health benefits) than they are to risk loss of financial data (for an anticipated return in financial benefits). We also find that 50%–60% of data recipients choose to protect another person's data, with no significant differences between frames.