Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change

What changes in the distribution of crop yields occur as a result of technological innovation? Viewing observed yields as random variables, estimation of the yield distribution conditional on time provides one approach for characterizing distributional transformation. Yields are also affected by weather and other covariates, spatial correlation, and a paucity of data in any one location. Common parametric and nonparametric methods rarely consider these aspects in a unified manner. Comprehensive solutions for describing the distribution of yields can be considered ideal. We implement a Bayesian spatial quantile regression model for the conditional distribution of yields that is distribution-free, includes weather (covariate) effects, smooths across space, and models the complete quantile process. Results provide insight into the temporal and spatial evolution of crop yields with implications for the measurement of technological change. Acknowledgement :


Issue Date:
2018-07
Publication Type:
Conference Paper/ Presentation
DOI and Other Identifiers:
Record Identifier:
https://ageconsearch.umn.edu/record/277253
Language:
English
JEL Codes:
Q18; G22




 Record created 2018-10-02, last modified 2020-10-28

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