Measurement of Yield distribution: A Time-Varying Distribution Model

Tsung Yu Yang

No 103422, 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania from Agricultural and Applied Economics Association

Abstract: Regarding the nature of yield data, there are two basic characteristics that need to be accommodated while we are about to model a yield distribution. The first one is the nonstationary nature of the yield distribution, which causes the heteroscedasticity related problems. The second one is the left skewness of the yield distribution. A common approach to this problem is based on a two-stage method in which the yields are detrended first and the detrended yields are taken as observed data modeled by various parametric and nonparametric methods. Based on a two-stage estimation structure, a mixed normal distribution seems to better capture the secondary distribution from catastrophic years than a Beta distribution. The implication to the risk management is the yield risk may be underestimated under the common selection -- Beta distribution. A mixed normal distribution under a time-varying structure, under which the parameters are allowed to vary over time, tends to collapse to a single normal distribution. The time-varying mixed normal model fits the realized yield data in one step that avoids the possible bias caused by sampling variability. Also, the time-varying parameters imply that the premium rates can be adjusted to represent the most recent information and that lifts the efficiency of the insurance market.

Keywords: Agricultural Finance; Crop Production/Industries; Research Methods/Statistical Methods; Risk and Uncertainty (search for similar items in EconPapers)
Pages: 20
Date: 2011
New Economics Papers: this item is included in nep-ecm and nep-ias
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aaea11:103422

DOI: 10.22004/ag.econ.103422

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