Measurement of Yield distribution: A Time-Varying Distribution Model

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dc.creatorYang, Tsung Yu
dc.date2017-04-01T20:09:00Z
dc.date.accessioned2026-07-09T05:33:45Z
dc.descriptionRegarding 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.
dc.identifierdoi:10.22004/ag.econ.103422
dc.identifierhttps://ageconsearch.umn.edu/record/103422/files/13637_Yield%20Distribution_2011%20AAEA.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/103422
dc.identifier.urihttp://hdl.handle.net/123456789/565336
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/103422
dc.titleMeasurement of Yield distribution: A Time-Varying Distribution Model
dc.typeText

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