Since its identification in April 2009 an A(H1N1) virus containing a unique combination of gene segments from both North American and Eurasian swine lineages has continued to circulate in humans. The 2009 A(H1N1) virus is distantly related to its nearest relatives, indicating that its gene segments have been circulating undetected for an extended period. Low genetic diversity among the viruses suggests the introduction into humans was a single event or multiple events of similar viruses. Molecular markers predicted for adaptation to humans are not currently present in 2009 A(H1N1) viruses, suggesting previously unrecognized molecular determinants could be responsible for the transmission among humans. Antigenically the viruses are homogeneous and similar to North American swine A(H1N1) viruses but distinct from seasonal human A(H1N1).

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Parameter estimation with non-ignorable missing data is a challenging problem in statistics. The fully parametric approach for joint modeling of the response model and the population model can produce results that are quite sensitive to the failure of the assumed model. We propose a more robust modeling approach by considering the model for the nonresponding part as an exponential tilting of the model for the responding part. The exponential tilting model can be justified under the assumption that the response probability can be expressed as a semi-parametric logistic regression model.In this paper, based on the exponential tilting model, we propose a semi-parametric estimation method of mean functionals with non-ignorable missing data. A semiparametric logistic regression model is assumed for the response probability and a non-parametric regression approach for missing data discussed in Cheng (1994) is used in the estimator. By adopting nonparametric components for the model, the estimation method can be made robust. Variance estimation is also discussed and results from a simulation study are presented. The proposed method is applied to real income data from the Korean Labor and Income Panel Survey.

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We examine the performances of several popular Lévy jump models and some of the most sophisticated affine jump-diffusion models in capturing the joint dynamics of stock and option prices. We develop efficient Markov chain Monte Carlo methods for estimating parameters and latent volatility/jump variables of the Lévy jump models using stock and option prices. We show that models with infinite-activity Lévy jumps in returns significantly outperform affine jump-diffusion models with compound Poisson jumps in returns and volatility in capturing both the physical and risk-neutral dynamics of the S&P 500 index. We also find that the variance gamma model of Madan, Carr, and Chang with stochastic volatility has the best performance among all the models we consider.

We attempt to answer two questions in this paper: (i) How did jumps in equity returns change after the 2008-2009 financial crisis-in particular, were there significant changes in jump rates or in jump sizes, or both? (ii) Can the performance of affine jumpdiffusion models be improved if jump sizes are larger, i.e., jumps with tails heavier than those of the normal distribution? To answer the second question, we find that a simple affine jump-diffusion model with both stochastic volatility and double-exponential jumps fits both the S&P 500 and the NASDAQ-100 daily returns from 1980 to 2013 well; the model outperforms existing ones (e.g., models with variance-gamma jumps or jumps in volatility) during the crisis and is at least comparable before the crisis. For the first question, on the basis of the model and the data sets, we observe that during the crisis, negative jump rate increased significantly, although there was little change in the average negative jump size.

While controversy surrounds skewness attributes of typical yield distributions, a better understanding is important for agricultural policy assessment and for crop insurance rate setting. Day (1965) conjectured that crop yield skewness declines with an increase in low levels of nitrogen use, but higher levels have no effect. In a theoretical model based on the law of the minimum (von Liebig) technology, we find conditions under which Day's conjecture applies. Employing four experimental plot datasets, we investigate the conjecture by introducing (a) a flexible Bayesian extension of the Just-Pope technology to incorporate skewness, and (b) a quantile-based measure of skewness shift. For corn yields, the Bayesian estimation provides strong evidence in favor of negative skewness at commercial nitrogen rates and for Day's conjecture. There was weaker evidence in favor of positively skewed cotton yield and little evidence in favor of the conjecture. The results are also confirmed by the quantile-based measure. AbstractWhile controversy surrounds skewness attributes of typical yield distributions, a

Analysis of crop yield distributions provides insights into better understanding how natural resources shape agricultural productivity. This study seeks to provide a rigorous theoretical and empirical understanding of the effects of exogenous geographic and climatic factors on the first three moments of crop yields with focus on the third moment. We hypothesize that exogenous factors having beneficial effects on crop production should make crop yield distributions less positively or more negatively skewed. We employ a large crop insurance data set for corn, soybean, and wheat to find general support for our natural-resources-determines-skewness hypothesis. The proposed statistical method optimally uses correlations between the first three moments. It significantly improves estimation performance over existing methods, including the linear moment model which has been commonly applied in the literature.JEL classifications: Q10, Q18, Q50