Crop-Yield Distributions Revisited
TLDR
This paper revisited the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality.Abstract:
This article revisits the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality. It concludes that some aggregate and farm-level yield distributions are nonnormal, kurtotic, and right or left skewed, depending on the circumstances. The advantages of utilizing nonnormal versus normal probability distribution function models, and the consequences of incorrectly assuming crop-yield normality are explored.read more
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Rice Yield Distribution and Risk Assessment in South Asian Countries: A Statistical Investigation
TL;DR: In this article, the authors examined the rice yield distributions, estimate yield risks at country level, and compare risks between five countries namely Afghanistan, Bangladesh, Nepal, Sri Lanka, and Pakistan, and applied the Anderson Darling (AD) test to test the goodness-of-fit for four distributions by using country level de-trended rice yields from 1961 to 2010.
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WHAT CAN WE INFER ABOUT FARM-LEVEL CROP YIELD PDF's FROM COUNTY-LEVEL PDF's?
TL;DR: In this paper, the authors investigated the relationship between farm and county yield distributions using both statistical theory and the Monte-Carlo simulation method, and showed that under suitable farm yield correlation and density structures, the shape of yield distribution at the farm level is similar to that at the county level.
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Contemporary Issues in Estimating Yield Distributions
TL;DR: This article used historical county corn yield data for Arkansas and Louisiana and nonparametric methods to show the importance of data transformation in crop risk analysis and demonstrate that inappropriate data treatment can lead to misestimation of probability density estimates.
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Issues and Strategies for Aggregate Supply Response Estimation for Policy Analyses
TL;DR: In this article, the use of the small-sample econometrics principles and strategies to come up with reliable yield and acreage models for policy analyses was demonstrated. But the importance of proper representation of systematic and random components of the model for improving forecasting precision along with more reliable confidence intervals for the forecasts was not emphasized.
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On agricultural commodities’ extreme price risk
TL;DR: This paper applied extreme value theory to estimate the size and likelihood of price spikes in agricultural commodities, and showed that the eight agricultural commodities in their sample exhibit fat-tailed return distributions, and statistical tests confirm the heavy-tailedness of prices for agricultural commodities.
References
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Book
Continuous univariate distributions
TL;DR: Continuous Distributions (General) Normal Distributions Lognormal Distributions Inverse Gaussian (Wald) Distributions Cauchy Distribution Gamma Distributions Chi-Square Distributions Including Chi and Rayleigh Exponential Distributions Pareto Distributions Weibull Distributions Abbreviations Indexes
Book
Introduction to the Theory of Statistics
TL;DR: In this article, a tabular summary of parametric families of distributions is presented, along with a parametric point estimation method and a nonparametric interval estimation method for point estimation.
Journal ArticleDOI
Introduction to Mathematical Statistics.
TL;DR: This chapter discusses distributions in the context of Bayesian Statistics, which aims to clarify the role of randomness in the construction of statistical inference.
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Are Crop Yields Normally Distributed
Richard E. Just,Quinn Weninger +1 more
TL;DR: In this paper, the evidence for nonnormality of crop yields is reassessed and three methodological problems are identified in typical yield distribution analyses: misspecification of the nonrandom components of yield distributions, missreporting of statistical significance, and use of aggregate timeseries (ATS) data to represent farm-level yield distributions.
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Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts
TL;DR: In this article, nonparametric density estimation procedures were used to evaluate county-level crop yield distributions and their implications for rating area-yield crop insurance contracts were discussed, and the procedures developed are used to measure yield risk and calculate insurance premium rates for wheat and barley in the 1995-96 Group Risk Program.
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