Determinants of India’s Food Grain Production: Evidence from Quantile Autoregressive Distributed Lag Model

Abstract

This study intends to identify the determinants of food grain production in India by employing Quantile Autoregressive Distributed Lag model of Cho et al. (2014). QARDL modelling approach simultaneously captures both the long-run relationship and the associated short-run dynamics across a range of quantiles of the conditional distribution of the dependent variable in a fully parametric setting. The strength of the QARDL model has been shown in the empirical assessment of food grain production using the time series data of rainfall, fertilizer use, and pesticide consumption. It is found that rainfall has an asymmetric impact on food grain production.