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Of Particles Molecules: Application of Particle Filtering to Irrigated Agriculture in Punjab, India

Author

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  • Alban Thomas

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

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).

Suggested Citation

  • Alban Thomas, 2021. "Of Particles Molecules: Application of Particle Filtering to Irrigated Agriculture in Punjab, India," Post-Print hal-03299110, HAL.
    • Handle: RePEc:hal:journl:hal-03299110
    DOI: 10.1007/978-3-030-73249-3_35
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