A Study on Cointegration of Monthly Prices of Turmeric in Kadapa and Duggirala Market of Andhra Pradesh, India

Abstract

Aims: To estimate both short-run and long-run equilibrium relationship and identify the long run uni-directional or bi-directional causality for the given markets. Place and Duration of Study: In the present study, monthly turmeric prices were collected over a period of 185 months, from January 2010 to May 2025, for the selected markets of Kadapa and Duggirala in Andhra Pradesh. The data was collected from the official Agmarknet website (https://agmarknet.gov.in). Methodology: The Augmented Dickey Fuller test is used for detecting stationarity of time series. The Johansen cointegration test was applied prior to Vector Error Correction specification, to confirm that the variables are cointegrated and to determine the number of cointegrating equations. Vector Error Correction Model had been used to analyze the short-run and long-run dynamics in the model. Granger causality test had been used to identify the causal relationship between the price series of different markets. Results and Conclusion: Present study made an attempt to study the market integration of Kadapa and Duggirala. It was found that the two markets were integrated of order one by using ADF test and Johansen’s Cointegration test, one prices cointegrating vector between the Kadapa and Duggirala market of turmeric, implies they share a long-run equilibrium relationship. From VECM estimates, Error Correction Term (ECT), it was identified that the price adjustment from short run disequilibrium to long run equilibrium by 24.6% per month respectively and in short run, the current month price of Kadapa market was influenced by one month lagged price of turmeric of its own. Granger Causality test revealed that there existed long run bi-directional causality for markets of Kadapa to Duggirala prices of turmeric. By the study of long-run equilibrium relationship and bi-directional price causality, helps in understanding the price fluctuations in one market influence others and the presence of similar relationships between the markets is identified and it is essential for policymakers. This relationship is helpful for better forecasting, risk management and the formulation of effective economic policies. Specifically, it identifies the direction and strength of price transmission between markets can help policymakers anticipate potential impacts of economic shocks and implement appropriate interventions.