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Yield trend estimation in the presence of non-constant technological change and weather effects 31
Conradt, Sarah; Bokusheva, Raushan; Finger, Robert; Kussaiynov, Talgat.
The application of yield time series in risk analysis prerequisites the estimation of technological trend which might be present in the data. In this paper, we show that in presence of highly volatile yield time series and non-constant technology, the consideration of the weather effect in the trend equation can seriously improve trend estimation results. We used ordinary least squares (OLS) and MM, a robust estimator. Our empirical analysis is based on weather data as well as farm-level and county-level yield data for a sample of grain-producing farms in Kazakhstan.
Tipo: Presentation Palavras-chave: Yield detrending; Weather information; Robust trend estimation; Aggregation; Risk and Uncertainty; Q19.
Ano: 2012 URL: http://purl.umn.edu/122541
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Catastrophic crop insurance effectiveness: does it make a difference how yield losses are conditioned? 31
Bokusheva, Raushan; Conradt, Sarah.
The study evaluates the effectiveness of a catastrophic drought-index insurance developed by applying two alternative methods - the standard regression analysis and the copula approach. Most empirical analyses obtain estimates of the dependence of crop yields on weather by employing linear regression. By doing so, they assume that the sensitivity of yields to weather remains constant over the whole distribution of the weather variable and can be captured by the effect of the weather index on the yield conditional mean. In our study we evaluate, whether the prediction of farm yield losses can be done more accurately by conditioning yields on extreme realisations of a weather index. Therefore, we model the dependence structure between yields and weather by...
Tipo: Presentation Palavras-chave: Catastrophic insurance; Weather-based insurance; Copula; Risk and Uncertainty; C18; Q14.
Ano: 2012 URL: http://purl.umn.edu/122443
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