 Of Particles Molecules: Application of Particle Filtering to Irrigated Agriculture in Punjab, IndiaPost-Print from HAL Abstract: We present an estimation method for agricultural crop yield functions, when unobserved productivity depends on water availability that is only partially observed. Using the setting of Bayesian non-linear filtering for estimating Hidden Markov Models, we discuss joint estimation of state variables and parameters in a structural production model with potentially endogenous regressors. An extension to particle filtering with resampling, convolution filter based on kernel regularization, is then discussed. We apply this non-parametric method to estimate a system of structural equations for rice crop yield and unobserved productivity on panel data for 10 districts in Punjab, India. Results based on computer-intensive resampling steps illustrate the interest of convolution particle filtering techniques, with low interquartile range of time-varying estimates. We compare fertilizer elasticity estimates with and without accounting for unobserved productivity, and we find a significant relationship between unobserved productivity and nitrogen fertilizer input, when the former is conditioned on district-level climate variables (summer rainfall, potential evapotranspiration). Date: 2021-06 Published in Advances in Contemporary Statistics and Econometrics, Springer International Publishing, pp.691-710, 2021, 978-3-030-73248-6. ⟨10.1007/978-3-030-73249-3_35⟩ There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03299110 DOI: 10.1007/978-3-030-73249-3_35 Access Statistics for this paper More papers in Post-Print from HAL |
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