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FOOD PRICE VOLATILITY: A Comparative Analysis among Major Cities of Pakistan

Author

Listed:
  • Nigar ZEHRA*
  • Ambreen FATIMA**

Abstract

The purpose of this study is to measure the food price volatility for sixteen food commodities [beef, chicken, pulse mash, pulse moong, pulse masoor, rice iri, wheat, tomato, potato, onion, ginger, garlic, milk, egg, sugar and tea] for fourteen main cities of Pakistan [Bahawalpur, Faisalabad, Hyderabad, Islamabad, Karachi, Khuzdar, Lahore, Multan, Peshawar, Quetta, Rawalpindi, Sargodha, Sialkot and Sukkur]. Furthermore, to provide comparative analyses of volatilities among different cities GARCH (1,1), I GARCH (1,1) and standard deviation techniques are employed on the monthly food price data for the period July 2002 to June 2016 collected from various issues of Pakistan Bureau of Statistics for fourteen cities. The results elaborate that volatility is exist in the series of food prices with strong heterogeneity among cities. It is suggested that the government should develop a mechanism to keep a check on the variation in prices and design separate policies for each city according to the volatility in the prices of food commodities in that city.

Suggested Citation

  • Nigar ZEHRA* & Ambreen FATIMA**, 2019. "FOOD PRICE VOLATILITY: A Comparative Analysis among Major Cities of Pakistan," Pakistan Journal of Applied Economics, Applied Economics Research Centre, vol. 29(1), pages 71-91.
    • Handle: RePEc:pje:journl:article29sumiv
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    References listed on IDEAS

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    1. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
    2. Apergis, Nicholas & Rezitis, Anthony, 2011. "Food Price Volatility and Macroeconomic Factors: Evidence from GARCH and GARCH-X Estimates," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 43(1), pages 95-110, February.
    3. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Headey, Derek D., 2014. "Food prices and poverty reduction in the long run:," IFPRI discussion papers 1331, International Food Policy Research Institute (IFPRI).
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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