Yield trend estimation in the presence of non-constant technological change and weather effects

S. Conradt; R. Bokusheva; R. Finger; T. Kussaiynov

Yield trend estimation in the presence of non-constant technological change and weather effects

S. Conradt, R. Bokusheva, R. Finger, T. Kussaiynov

Research output: Contribution to conference › Conference paper › Academic

Abstract

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.

Original language	English
Publication status	Published - 2012
Event	123rd EAAE Seminar "Price Volatility and Farm Income Stabilisation" - Duration: 23 Feb 2012 → 24 Feb 2012

Seminar

Seminar	123rd EAAE Seminar "Price Volatility and Farm Income Stabilisation"
Period	23/02/12 → 24/02/12

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Cite this

@conference{3a8a9aa97b5842a6816933361f4d497c,

title = "Yield trend estimation in the presence of non-constant technological change and weather effects",

abstract = "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.",

author = "S. Conradt and R. Bokusheva and R. Finger and T. Kussaiynov",

year = "2012",

language = "English",

note = "123rd EAAE Seminar {"}Price Volatility and Farm Income Stabilisation{"} ; Conference date: 23-02-2012 Through 24-02-2012",

}

TY - CONF

T1 - Yield trend estimation in the presence of non-constant technological change and weather effects

AU - Conradt, S.

AU - Bokusheva, R.

AU - Finger, R.

AU - Kussaiynov, T.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

M3 - Conference paper

T2 - 123rd EAAE Seminar "Price Volatility and Farm Income Stabilisation"

Y2 - 23 February 2012 through 24 February 2012

ER -

Yield trend estimation in the presence of non-constant technological change and weather effects

Abstract

Seminar

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