TY  - EJOUR
AB  - This article develops a procedure for weighting historical loss cost experience based on longer
time-series weather information. Using a fractional logit model and out-of-sample competitions,
weather variables are selected to construct an index that allows proper assessment of the relative
probability  of  weather  events  that  drive  production  losses  and  to  construct  proper  “weather
weights” that are used in averaging historical loss cost data. A variable-width binning approach
with equal probabilities is determined as the best approach for classifying each year in the shorter
historical loss cost data used for rating. When the weather-weighting approach described above
is applied, we find that the weather-weighted average loss costs at the national level are different
from the average loss costs without weather weighting for all crops examined.
AU  - Rejesus, Roderick M.
AU  - Coble, Keith H.
AU  - Miller, Mary France
AU  - Boyles, Ryan
AU  - Goodwin, Barry K
AU  - Knight, Thomas O.
DA  - 2015-05
DA  - 2015-05
DO  - 10.22004/ag.econ.206598
DO  - doi
EP  - 324
EP  - 306
ID  - 206598
IS  - 2
JF  - Journal of Agricultural and Resource Economics
KW  - Crop Production/Industries
KW  - Crop insurance
KW  - premium rating
KW  - weather weighting
L1  - https://ageconsearch.umn.edu/record/206598/files/JAREMay20157Rejesuspp306-324.pdf
L2  - https://ageconsearch.umn.edu/record/206598/files/JAREMay20157Rejesuspp306-324.pdf
L4  - https://ageconsearch.umn.edu/record/206598/files/JAREMay20157Rejesuspp306-324.pdf
LA  - eng
LK  - https://ageconsearch.umn.edu/record/206598/files/JAREMay20157Rejesuspp306-324.pdf
N2  - This article develops a procedure for weighting historical loss cost experience based on longer
time-series weather information. Using a fractional logit model and out-of-sample competitions,
weather variables are selected to construct an index that allows proper assessment of the relative
probability  of  weather  events  that  drive  production  losses  and  to  construct  proper  “weather
weights” that are used in averaging historical loss cost data. A variable-width binning approach
with equal probabilities is determined as the best approach for classifying each year in the shorter
historical loss cost data used for rating. When the weather-weighting approach described above
is applied, we find that the weather-weighted average loss costs at the national level are different
from the average loss costs without weather weighting for all crops examined.
PY  - 2015-05
PY  - 2015-05
SP  - 306
T1  - Accounting for Weather Probabilities in Crop Insurance Rating
TI  - Accounting for Weather Probabilities in Crop Insurance Rating
UR  - https://ageconsearch.umn.edu/record/206598/files/JAREMay20157Rejesuspp306-324.pdf
VL  - 40
Y1  - 2015-05
T2  - Journal of Agricultural and Resource Economics
ER  -