SPATIAL INTEGRATION AND PRICE FORECASTING ANALYSIS OF MAJOR TOMATO WHOLESALE MARKETS IN INDIA: AN APPLICATION OF SARIMA MODEL 3541
dc.contributor.advisor | B. SWAMINATHAN | |
dc.contributor.author | 2010120073 | |
dc.contributor.other | 2010120073 | |
dc.date.accessioned | 2023-12-05T16:00:02Z | |
dc.date.available | 2023-12-05T16:00:02Z | |
dc.date.issued | 2022-09 | |
dc.description.abstract | Tomato, Growth rate, instability, Trends, Seasonality, Market integration, Granger causality, Price shocks, forecasting Tomato is the second most important vegetable crop under cultivation next only to potatoes in the world. The present study has been undertaken to analyze the growth rate, instability, the behaviour of arrivals and prices, market integration, market behaviour to price shocks among the tomato markets. Major eight tomato markets of India were selected based on the largest average volume of arrivals during the study period. Time series data regarding monthly tomato arrivals and prices in selected markets were collected for the period 2010 to 2021 from secondary sources. Appropriate econometric tools like CGR, CDVI, trend analysis, seasonal indices, johansen cointegration, granger causality test, VECM, impulse response function and variance decomposition besides, SARIMA model were employed to meet the objectives of the study. In overall, growth rate of arrivals showed that Kolar market recorded a highest positive significant value of 0.11 per cent and in case of prices Coochbehar registered non-significant highest growth rate of 0.21 per cent. In month wise also Kolar market showed higher growth rate of arrivals compared to other markets and in case of prices Coochbehar showed higher growth rate with more than 10 per cent in all the months. Instability analysis of arrivals and prices showed that the variability of arrivals is higher than the prices. The trend component of arrivals showed negative trend except in Azadpur and Kolar market while in case of prices all the study markets showed highly significant positive values over the study period the with the highest annual increase in the prices was observed in the Coochbehar market (Rs.305.04/q). The seasonal indices of tomato arrivals had not shown a unique seasonal pattern while prices of study markets showed a uniform pattern with highest during July. Correlation coefficient of market arrivals and prices over the months and years showed the inverse relationship between them with some exceptions. The stationarity tests showed that the all the markets were stationary at level itself. Engle-granger and Johansen cointegration test confirmed the presence of long run relationship with each other and thus are integrated so that there is a smooth transmission of prices across the spatially separated markets. To ascertain the speed of adjustment among the markets for long run equilibrium vector error correction model was employed the coefficient of error correction term of Azadpur market revealed that about 56 per cent adjustment towards the long run equilibrium occurs in the period of one month. Granger causality test revealed the presence of bidirectional relationship between the pair’s viz., Azadpur– Ahmedabad, Kanpur-Azadpur, Coochbehar-Bargarh, Kanpur-Bargarh and Kanpur Coochbehar. Kolar market was the lead tomato market as it was influencing prices of other selected tomato markets of India except Coochbehar market. Impulse response function showed that the one-unit standard deviation shock to any of the market result in the immediate decline in all the other selected markets during the past 4-5 months thereafter the prices getting stabilized with exception in Coochbehar market the shock originating from Coochbehar is less transmitted to the other selected tomato markets. Variance decomposition analysis results showed that impact of variable on self is greater than the impacted caused by other variables in all the selected tomato markets of India. In terms of price forecasting, SARIMA models were found to be the best fit among different models to forecast tomato prices in all the markets. The SARIMA model (1,0,3) (1,0,2)12 was selected for predicting onward tomato prices in Ahmedabad (Gujarat) market while (2,0,1) (1,0,1)12, (3,0,2) (2,1,2)12, (2,0,3) (1,0,1)12, (3,0,3) (2,0,1)12, (2,0,6) (1,1,3)12, (2,0,4) (1,0,1)12, and (2,0,2) (1,1,0)12 were selected for Azadpur (New Delhi), Bargarh (Odisha) and Coochbehar (West Bengal) markets, Kanpur (Uttar Pradesh), Kolar (Karnataka), Mulakalacheruvu (Andhra Pradesh) and Nashik (Maharashtra). Similarly, SARIMA models were found useful for forecasting of tomato prices in all the markets with MAPE values ranging from 17.7% in Azadpur (New Delhi) market to 36.3% in Mulakalacheruvu (Andhra Pradesh) market | |
dc.identifier.uri | https://krishikosh.egranth.ac.in/handle/1/5810202227 | |
dc.language.iso | English | |
dc.pages | 215 | |
dc.publisher | JAU JUNAGADH | |
dc.sub | Agricultural Economics | |
dc.theme | SPATIAL INTEGRATION AND PRICE FORECASTING ANALYSIS OF MAJOR TOMATO WHOLESALE MARKETS IN INDIA: AN APPLICATION OF SARIMA MODEL 3541 | |
dc.these.type | M.Sc | |
dc.title | SPATIAL INTEGRATION AND PRICE FORECASTING ANALYSIS OF MAJOR TOMATO WHOLESALE MARKETS IN INDIA: AN APPLICATION OF SARIMA MODEL 3541 | |
dc.type | Thesis |