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The purpose of this paper is to bring out the development of a flexible model for nonstationary crop yield distributions and its applications to decision-making in crop insurance.

The authors design a nonparametric Bayesian approach based on Gaussian process regressions to model crop yields over time. Further flexibility is obtained via Bayesian model averaging that results in mixed Gaussian processes.

Simulation results on crop insurance premium rates show that the proposed method compares favorably with conventional estimators, especially when the underlying distributions are nonstationary.

Unlike conventional two-stage estimation, the proposed method models nonstationary crop yields in a single stage. The authors further adopt a decision theoretic framework in its empirical application and demonstrate that insurance companies can use the proposed method to effectively identify profitable policies under symmetric or asymmetric loss functions.

In this paper we investigate the joint conditional distribution of health (life expectancy) and income growth, and its evolution over time. The conditional distributions of these two variables are obtained by applying non-parametric methods to a bivariate non-parametric regression system of equations. Analyzing the distributions of the non-parametric fitted values from these models we find strong evidence of movement over time and strong evidence of first-order stochastic dominance of the earlier years over the later ones. We also find strong evidence of second-order stochastic dominance by non-OECD countries over OECD countries in each period. Our results complement the findings of Wu, Savvides and Stengos (2008) who explored the unconditional behaviour of these joint distributions over time.

Knowledge of the dependence structure between financial assets is crucial to improve the performance in financial risk management. It is known that the copula completely summarizes the dependence structure among multiple variables. We propose a multivariate exponential series estimator (ESE) to estimate copula densities nonparametrically. The ESE has an appealing information-theoretic interpretation and attains the optimal rate of convergence for nonparametric density estimations in Stone (1982). More importantly, it overcomes the boundary bias of conventional nonparametric copula estimators. Our extensive Monte Carlo studies show the proposed estimator outperforms the kernel and the log-spline estimators in copula estimation. It also demonstrates that two-step density estimation through an ESE copula often outperforms direct estimation of joint densities. Finally, the ESE copula provides superior estimates of tail dependence compared to the empirical tail index coefficient. An empirical examination of the Asian financial markets using the proposed method is provided.

We propose a nonparametric estimator of the Lorenz curve that satisfies its theoretical properties, including monotonicity and convexity. We adopt a transformation approach that transforms a constrained estimation problem into an unconstrained one, which is estimated nonparametrically. We utilize the splines to facilitate the numerical implementation of our estimator and to provide a parametric representation of the constructed Lorenz curve. We conduct Monte Carlo simulations to demonstrate the superior performance of the proposed estimator. We apply our method to estimate the Lorenz curve of the U.S. household income distribution and calculate the Gini index based on the estimated Lorenz curve.

The purpose of this paper is to develop information on the relative efficiency of Farm Credit System (FCS) lenders. Also the evolution of relative efficiency is examined as influenced by the biofuel boom, the financial crisis, and farm income increases. The paper aims to discuss these issues.

A stochastic frontier production function is used to estimate technical efficiency of FCS banks and associations.

A significant difference is found in efficiency between large and small associations and banks. Larger asset bases and management compensation are found to be positively associated with efficiency. Banks are found to have higher technical efficiency than associations (66-46 percent). Association efficiency is found to be increasing indicating likely effects of recent consolidation. The financial crisis was not found to have a significant effect with the bioenergy and farm income booms being likely countervailing forces.

Further work is needed on the impact of the biofuel boom, increases in farm income, and new regulations.

The study provides information and indications of strategies for FCS management including additional consolidation.

This does an updated assessment of FCS efficiency taking into account changes in consolidation, lending practices, and economic conditions. Implications are developed for management actions such as more consolidation. The study also uses a more advanced methodology compared to older studies.

Identification and inference are central to applied analysis, and two papers examine these issues, the first being theoretical in nature and the second being simulation based.