Yield trend estimation in the presence of non-constant technological change and weather effects
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.
|Date of creation:||23 Feb 2012|
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- Finger, Robert, 2012. "Biases in Farm-Level Yield Risk Analysis due to Data Aggregation," Journal of International Agricultural Trade and Development, Journal of International Agricultural Trade and Development, vol. 61(1).
- Robert Finger, 2010. "Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 205-211.
- Antoine Leblois & Philippe Quirion, 2013. "Agricultural insurances based on meteorological indices: realizations, methods and research challenges," Post-Print hal-00656778, HAL.
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