Analysing farmland rental rates using Bayesian geoadditive quantile regression
利用德国农业普查数据，采用贝叶斯地理加性分位数回归方法，半参数地建模农田租金率的条件分位数，揭示了协变量的非线性影响及空间结构，为农业经济学研究提供了更细致的洞察。
Empirical studies on farmland rental rates so far have predominantly concentrated on modelling conditional means using spatial autoregressive models. While these models only focus on the central tendency of the response variable, quantile regression provides more detailed insight by modelling different points of the conditional distribution as a function of covariates. Based on data from the German agricultural census, this article contributes to the agricultural economics literature by modelling conditional quantiles of farmland rental rates semi-parametrically using Bayesian geoadditive quantile regression. Our results stress the importance of using semi-parametric regression models, as several covariates influence rental rates in an explicit non-linear way. Moreover, our analysis allows us to uncover potential heterogeneities of the estimated effects across the conditional distribution of rental rates. By explicitly modelling and visually presenting the spatial effects, we also provide additional insight into the spatial structure of German farmland rental rates.