Panel Data Modeling for Indian Food Grain Production

Abstract

The present investigation was carried out to study the food grain production trends in selected states of India for the period 2001-02 to 2020-2021. In this study a panel data regression model is used to combat the complicated relations and strong auto-correlation present in the crop production time series data. Panel data modelling control the heterogeneity of cross-sectional units over time. The results reveal that between state-to-state food grain production is highly significant, the highest food grain production was registered in Uttar Pradesh, followed by Punjab and Madhya Pradesh. Very lowest was registered in Kerala and Himachal Pradesh. Levin, Lin & Chu t statistics values are found to be significant indicating that the variable under study is stationary at level and hence the variable under study is I(0).Among the three different models viz., constant coefficient model, fixed effect model and random effect model, the fixed effect model was selected as an appropriate trend model as per the Hausman test. Based on the statistical significance of the estimated coefficients and the substantial increase in the R2 value to 95%, the fixed effect model or the least-square dummy variable regression model performs better than the panel least square regression model. The Hausman test confirms the selection of fixed effect over the random effect model. The fixed effects are positive in Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Gujarat, Chhattisgrah, Bihar, Haryana and Madhya Pradesh. Negative fixed effects are observed in Himachal Pradesh, Jharkhand, Maharashtra, Odisha, Punjab, Rajasthan, Uttar Pradesh, Uttarakhand and West Bengal. Increases in food grain production have been observed