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.
|Publication status||Published - 2012|
|Event||123rd EAAE Seminar "Price Volatility and Farm Income Stabilisation" - |
Duration: 23 Feb 2012 → 24 Feb 2012
|Seminar||123rd EAAE Seminar "Price Volatility and Farm Income Stabilisation"|
|Period||23/02/12 → 24/02/12|
Conradt, S., Bokusheva, R., Finger, R., & Kussaiynov, T. (2012). Yield trend estimation in the presence of non-constant technological change and weather effects. Paper presented at 123rd EAAE Seminar "Price Volatility and Farm Income Stabilisation", . http://ideas.repec.org/p/ags/eaa123/122541.html