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There are many situations that we need to model as risky prospects whose values are censored below at 0, hence defined on semi-infinite support. As useful tools for such models, we derive analytic characterizations of risks such as certainty equivalent and risk premium, for gamma and lognormal distributions and utility functions that have constant risk aversion. As a main application, we consider an extension of the `linear contract, exponential utility, normally distributed risks' (LEN) moral hazard model to gamma distributed risks. We also discuss other potential applications, ranging from loan contracts to comparison of income distributions.

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We consider the estimation and inference of unbalanced panel data models with cross-sectional dependence with a large number of individual units in a relatively short time period. By following the common correlated effects (CCE) approach of Pesaran (2006), we propose a CCE estimator for unbalanced panels (CCE-UB). The asymptotics of the CCE-UB estimator is developed in the paper. Small scale Monte Carlo simulation is conducted to examine the finite sample properties of the proposed estimator.

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This paper examines the tax and expenditure mix in environmental public finance management. We propose the notion of the policy mix and derive the structure of the policy mix. We then perform a small-scale simulation to deliver some practical implications.

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This paper introduces and analyzes a new model for realized volatility that accommodates endogenous regime switching. The model is based on the heterogeneous autoregressive model and allows for two-state regime switching. Importantly, a current shock to the realized volatility affects the regime switching in the next period. We apply the model to the realized volatility of the daily S$&$P 500 Index return series. The estimation result shows that the short-term volatility component is the most influential in the high volatility regime while the long-term volatility component is dominant in the low volatility regime. Moreover, the model significantly outperforms existing models in within-sample fitting.