Impact of copula choice on the modeling of crop yield basis risk

Woodard, Joshua D.; Paulson, Nicholas D.; Vedenov, Dmitry V.; Power, Gabriel J.

doi:10.1111/j.1574-0862.2011.00555.x

Cited by 23 publications

(18 citation statements)

References 33 publications

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“…The crop yield data need to be tested for a deterministic (or stochastic) time trend. Failure to identify and remove a time trend can cause inaccurate skewness estimations for yields (Claassen and Just, ; Finger, , ; Sherrick et al., ; Woodard et al., ). Yields can be upward trending over time due to technological changes, better management practices, and advances in seed biotechnology.…”

Section: Empirical Proceduresmentioning

confidence: 99%

“…Finger () compared several methods used to detrend yield data and found the M‐estimator to be an appropriate method. The M‐estimation method is commonly used to reweight time trend crop yield models (Finger, , ; Harri et al., ; Woodard and Sherrick, ; Woodard et al., ). If a time trend is significant in the mean and/or the variance, the time trend is removed by using standardized residuals to test for normality and calculate skewness at each nitrogen rate.…”

Section: Empirical Proceduresmentioning

confidence: 99%

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Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates

Boyer

Brorsen

Tumusiime

2014

Agricultural Economics

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Accurate modeling of skewness is needed to increase the actuarial fairness of crop insurance. We test Day's conjecture that crop yield skewness becomes negative as nitrogen rates increase and determine how well a linear response stochastic plateau (LRSP) production function matches the pattern of observed skewness using four long-term nitrogen experiments. Stillwater wheat is consistent with Day's conjecture, but the skewness for Lahoma and Altus wheat yields as well as Altus cotton yields are not. The LRSP assumes normal random effects and can explain only a small part of observed skewness, so a new LRSP with skew-normal random effects is introduced, which comes closer to explaining the observed skewness and should increase the accuracy of nitrogen rate recommendations. Negative skewness reduced optimal nitrogen rates and positive skewness increased optimal nitrogen rates.JEL classifications: C49, D24, Q19

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Section: Empirical Proceduresmentioning

confidence: 99%

Section: Empirical Proceduresmentioning

confidence: 99%

Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates

Boyer

Brorsen

Tumusiime

2014

Agricultural Economics

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“…A convenient choice for estimating price comovements is the Gaussian copula (Woodard et al 2011), which owes its popularity, in part, to its connection to the familiar multivariate normal distribution. The Gaussian copula also conveniently connects to linear regression set-ups, in that the measure of dependence in the Gaussian copula captures the same linear correlation reflected in the coefficients in linear regressions (Goodwin 2014).…”

Section: Introductionmentioning

confidence: 99%

Crop price comovements during extreme market downturns

Zimmer

2015

Aus J Agri & Res Econ

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This study develops and estimates mixture models of crop price comovements using copula functions, which allow for departures from normality during extreme market circumstances. The models also account for unique time-series patterns inherent in crop price data. The results point to two main conclusions. First, mixture models appear to provide an easy-to-estimate approach for capturing real-life crop price movements. Second, mixture models find that, during extreme market downswings, correlations in price movements strengthen by several orders of magnitude. These results suggest that structured securities assembled from different crops tend to lose diversified protection during extreme market downswings, the exact times when such protection is needed most.

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“…Along these lines, several alternatives to this trend removal approach have been suggested such as the use of time‐series components and the simultaneous estimation of time trends and yield distribution parameters (e.g., Bessler, 1982; Zhu et al, 2008). But, even though such alternative procedures may be superior in certain situations, detrending procedures consisting of trend identification and removal are predominantly used in current research and insurance applications (see, e.g., Claassen and Just, 2011; Woodard et al, 2010, 2011 for recent overviews). Furthermore, estimated trends in crop yield data can be used to specify expected yield levels for subsequent years and can thus be valuable, among others, for insurance applications.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the evaluation of alternative regression techniques conducted by Swinton and King (1991) and Finger (2010a) considered only specific estimators (the trimmed least squares and the MM‐estimator, respectively), not reflecting the wide range of estimators that are available and used in practice 3 . However, the comparison of different methodical approaches under a wide range of data situations can lead to an improvement of applications of crop insurance concepts (e.g., Woodard et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Investigating the performance of different estimation techniques for crop yield data analysis in crop insurance applications

Finger

2013

Agricultural Economics

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We investigate the performance of the OLS-, M-, MM-and the Theil-Sen-estimator for crop yield data analysis in crop insurance applications using Monte Carlo simulations. In particular, the performance is assessed with respect to trend estimation, prediction of future yield levels and the estimation of expected indemnity payments. In agreement with earlier findings, other estimators are found to be superior to OLS in simple regression problems if yield distributions are outlier contaminated and heteroscedastic. While this conclusion is also valid for subsequent applications such as yield prediction and the estimation of expected indemnity payments, the difference between the considered estimators becomes less distinct.For these applications, we find particularly the M-estimator to be a good compromise between high breakdown (very robust) estimators and the very efficient OLS-estimator.Because no regression technique dominates all others in all applications and scenarios for error term distributions, our results underline that the choice of the estimation technique should be dependent on the purpose of the crop yield data analysis. However, alternative estimators such as M-, MM-and Theil-Sen-estimator can reduce (and bound) the risk of unreliable or inefficient trend estimation and applications.3 1 Introduction and MotivationCrop yield insurances are of increasing importance in agriculture (Bielza et al., 2008, Ozaki andSilva, 2009). In order to reduce the potential of market failures, appropriate design and specification of insurance contracts is of outmost importance. In particular, the identification of crop yield distributions and its parameters is a crucial step for the estimation of insurance premiums and the specification of trigger yield levels. To quantify risk exposure in crop production, it is essential to identify and remove deterministic trends in time series of crop yield data because crop yield variability would be overestimated otherwise. Thus, the detrending of crop yield data is usually a central aspect in the analysis of crop yield data for insurance applications and the determination of crop yield distributions.However, several alternatives to this trend removal approach have been suggested in the literature. For instance, the use of time series components and the simultaneous estimation of time trends and yield distribution parameters have been suggested (e.g. Bessler, 1982, Zhu et al., 2008. The use of deterministic trend models has been also critically discussed in other economic applications (e.g. Nelson and Plosser, 1982 Finger, 2010b, Gallagher, 1987, Just and Weninger, 1999, Kaylen and Koroma, 1991, Swinton and King, 1991. Furthermore, the implications of employed methods in trend analysis on subsequent steps of data analysis such as yield prediction and estimation of expected insurance indemnities have not been investigated so far. These are, however, of particular importance because the detrending of crop yield data is usually only the first step in crop yield data analysis. ...

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Impact of copula choice on the modeling of crop yield basis risk

Cited by 23 publications

References 33 publications

Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates

Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates

Crop price comovements during extreme market downturns

Investigating the performance of different estimation techniques for crop yield data analysis in crop insurance applications

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