Analysing farmland rental rates using Bayesian geoadditive quantile regression

Empirical studies on farmland rental rates have predominantly concentrated on modelling conditional means using spatial autoregressive models, where a linear functional form be- tween the response and the covariates is assumed. This paper extends the hedonic pricing literature by modelling conditional quantiles of farmland rental rates semi-parametrically using Bayesian geoadditive quantile regression models. The flexibility of this model class overcomes the problems associated with functional form misspecifications and allows us to present a more detailed analysis. Our results stress the importance of making use of semi- parametric regression models as several covariates influence farmland rental rates in an ex- plicit non-linear way.

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 Record created 2017-04-01, last modified 2019-08-26

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