Mario J. Miranda

Area-Yield Crop Insurance Reconsidered

One of the more promising proposals for reforming the federal crop insurance program calls for both premium rates and indemnities to be based not on the producers individual yield but rather on the aggregate yield of a surrounding area. Area-yield crop insurance can provide more effective yield-loss coverage than individually tailored insurance, without most of the adverse selection and moral hazard problems that have historically undermined the actuarial performance of the federal crop insurance program.

Systemic Risk, Reinsurance, and the Failure of Crop Insurance Markets

Without affordable reinsurance, private crop insurance markets are doomed to fail because systemic weather effects induce high correlation among farm-level yields, defeating insurer efforts to pool risks across farms. Using an empirical model of the U.S. crop insurance market, we find that U.S. crop insurer portfolios are twenty to fifty times riskier than they would be otherwise if yields were stochastically independent across farms. We also find that area yield reinsurance contracts would enable crop insurers to cover most of their systemic crop loss risk, reducing their risk exposure to levels typically experienced by more conventional property liability insurers. Copyright 1997, Oxford University Press.

Innovations in Agricultural and Natural Disaster Insurance

Agricultural production has always been a risky endeavor. Farmers constantly have to deal with unfavorable weather conditions, variability in prices of inputs and outputs, livestock disease outbreaks, pests, etc. The uncertainty of future incomes complicates both short-term production decisions and long-term planning (e.g., expansion of production or capital investments in machinery and equipment). It also renders lending institutions less willing to provide loans to farmers, since the probability of default is relatively high. Although some forms of selfinsurance may be available to farmers (e.g., crop diversification or intertemporal income transfers), these have certain limitations and ultimately reduce farm profits in the long term.

El Nino-Southern Oscillation-based index insurance for floods: Statistical risk analyses and application to Peru

Received 23 June 2006; revised 18 June 2007; accepted 3 July 2007; published 17 October 2007. [1] Index insurance has recently been advocated as a useful risk transfer tool for disaster management situations where rapid fiscal relief is desirable and where estimating insured losses may be difficult, time consuming, or subject to manipulation and falsification. For climate-related hazards, a rainfall or temperature index may be proposed. However, rainfall may be highly spatially variable relative to the gauge network, and in many locations, data are inadequate to develop an index because of short time series and the spatial dispersion of stations. In such cases, it may be helpful to consider a climate proxy index as a regional rainfall index. This is particularly useful if a long record is available for the climate index through an independent source and it is well correlated with the

Estimation of Dynamic Nonlinear Rational Expectations Models of Primary Commodity Markets with Private and Government Stockholding

Stochastic-dynamic programming and disequilibrium maximum likelihood methods are combined to estimate a dynamic nonlinear rational expectations model of a market for a storable primary commodity. The estimation model captures the inherently nonlinear structure of private stockholding dynamics, the disequilibrium effects of government buffer stock intervention, and the impact of price expectations and risk on private supply and stockholding decisions. Copyright 1993 by MIT Press.

Wheat Storage and Trade in an Efficient Global Market

Domestic and international linkages in speculative stockholdings and trade of wheat are analyzed using a dynamic rational expectations model of the world wheat market dominated by the U.S. and the EU. The results demonstrate the importance of endogenizing both storage and trade in studying commodity markets and suggest that past government stockholdings have not followed efficient market outcomes. Results indicate that elimination of the Export Enhancement Program by the U.S. and of export restitution payments by the EU are unlikely to have a major impact on wheat exports from the two regions but will save millions of tax dollars in both regions. Copyright 1996, Oxford University Press.

Numerical solution of dynamic oligopoly games with capital investment

Summary. This paper discusses how numerical techniques may be used to solve the simultaneous functional equations that arise in general dynamic stochastic games. Unlike the conventional linear-quadratic approach, our methods may be used to address general model specifications that may include non-quadratic objective functions, non-linear equations of motion, and constraints on decision variables. As an illustration, we apply our methods to a dynamic duopoly game in which competing firms play short-run quantity game subject to production cost that can be lowered through investment in capital stock in the long run.

Optimal Forest Management with Carbon Sequestration Credits and Endogenous Fire Risk

We use a stochastic dynamic profit maximization model to investigate the effects of forest carbon sequestration credits on optimal forest management practices for stands facing wildfire risk. Landowners that periodically thin a stand can increase growth rates and mitigate loss of timber and carbon stocks from wildfire. Results indicate that thinning and shortening rotations are cost-effective strategies to mitigate wildfire risk. Carbon prices cause landowners to delay both their thinning treatments and the final rotation age. Thinning and extending timber rotations are thus a viable climate-change mitigation option even when stands are susceptible to risks of fire. (JEL Q23, Q54)

Numerical Strategies for Solving the Nonlinear Rational Expectations Commodity Market Model

In this paper, I compare the accuracy, efficiency and stability of different numerical strategies for computing approximate solutions to the nonlinear rational expectations commodity market model. I find that polynomial and spline function collocation methods are superior to the space discretization, linearization and least squares curve-fitting methods that have been preferred by economists in the past.

Maximum likelihood estimation of the nonlinear rational expectations asset pricing model

Abstract We introduce an efficient numerical algorithm for computing the full information maximum likelihood estimators of the nonlinear rational expectations asset pricing model. The algorithm calls for the Euler functional equation that characterizes the equilibrium path of asset returns to be re-solved using orthogonal polynomial collocation methods whenever the maximization routine perturbs the model parameters. Monte-Carlo sampling experiments indicate that the maximum likelihood estimator is substantially more efficient than the method of moments estimator whenever there is even a modest amount of noise in aggregate output data. This is true even if the output shock distribution is severely misspecified in the maximum likelihood estimation.