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Short Term Prediction of Agricultural Structural Change using Farm Accountancy Data Network and Farm Structure Survey Data

机译：利用农业会计数据网络和农业结构调查数据对农业结构变化的短期预测

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The prediction of farm structural change is of large interest at EU policy level, but available methods are limited regarding the joint and consistent use of available data sources. This paper develops a Bayesian Markov framework for short-term prediction of farm numbers that allows combining two asynchronous data sources in a single estimation. Specifically, the approach allows combining aggregated Farm Structure Survey (FSS) macro data, available every two to three years, with individual farm level Farm Accountancy Data Network (FADN) micro data, available on a yearly basis. A Bayesian predictive distribution is derived from which point predictions such as mean and other moments are obtained. The proposed approach is evaluated in an out-of-sample prediction exercise of farm numbers in German regions and compared to linear and geometric prediction as well as a "no-change" prediction of farm numbers. Results show that the proposed approach outperforms the geometric prediction but does not significantly improve upon the linear prediction and a prediction of no change in this context.
机译：在欧盟政策层面，对农场结构变化的预测非常重要，但是对于联合和一致地使用可用数据源而言，可用方法受到限制。本文开发了用于农场数量短期预测的贝叶斯马尔可夫框架，该框架允许将两个异步数据源组合在一个估计中。具体而言，该方法允许将每两到三年可获得的汇总农场结构调查（FSS）宏数据与每年可获得的单个农场级农场会计数据网络（FADN）微观数据进行组合。贝叶斯预测分布可从中得出，例如平均和其他矩点预测。在德国地区的农场数量的样本外预测演习中对提出的方法进行了评估，并将其与线性和几何预测以及农场数量的“不变”预测进行了比较。结果表明，所提出的方法优于几何预测，但在线性预测和在这种情况下无变化的预测方面却没有明显改善。

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