Banik, Nilanjan and Biswas, Basudeb (2012): The curious case of Indian agriculture. Forthcoming in:
Download (212Kb) | Preview
This paper examines association between the cyclical component of agricultural output and rainfall in India. When the cause of food inflation is because of supply shortage driven by inadequate rainfall and poor irrigation facilities, then a contractionary monetary policy may lead to stagflation. Considering agricultural output and rainfall data from four states in India we find evidence in favor of association.
|Item Type:||MPRA Paper|
|Original Title:||The curious case of Indian agriculture|
|Keywords:||Agriculture output, Beveridge-Nelson Decomposition, Rainfall, India|
|Subjects:||E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level; Inflation; Deflation|
|Depositing User:||Nilanjan Banik|
|Date Deposited:||07. May 2012 15:31|
|Last Modified:||12. Feb 2013 13:23|
1. Agenor, P-R., McDermott, J., and E. Prasad, (1999), “Macroeconomic Fluctuation in Developing Countries: Some Stylized Facts,” International Monetary Fund Working Papers, WP/99/35.
2. Baxter, M., and R., G., King, (1995), “Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series,” National Bureau of Economic Research, Working Paper No. 5022.
3. Beveridge, S. and C. R. Nelson, (1981), “A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention towards Measurement of Business Cycles” Journal of Monetary Economics, Volume 7, 151-174.
4. Blanchard, O.J. and D. Quah, (1989), “The Dynamic Effects of Aggregate Demand and Supply Disturbances,” American Economic Review, Volume 79 (4), 655-73.
5. Box, G.P. and M.G. Jenkins, (1976), “Time Series and Forecasting: An Applied Approach,” Oakland: Holden Day.
6. Central Statistical Organisation (2012), “Summary Data Real Sector,” Ministry of Statistics and Programme Implementation, Government of India.
7. Diebold, X., F., (1998), “The Past, Present, and Future of Macroeconomic Forecasting,” Journal of Economic Perspectives, Volume 12, 175-192.
8. Granger, C., J., (1996), “The Typical Spectral Shape of an Economic Variable,” Econometrica, Volume 34, 150-161.
9. Guay, A., and P., St-Amant, (1996), “Do Mechanical Filters Provide a Good Approximation of Business Cycles?” Bank Canada Technical Report No. 78.
10. Harvey, A., C., and A., Jaeger, (1993), “Detrending, Stylized Facts and the Business Cycles: An Empirical Investigation,” Journal of Applied Econometrics, Volume 8, 231-247.
11. Hodrick, R., J., and E., C., Prescott, (1997), “Post War US Business Cycles: An Empirical Investigation,” Journal of Money Credit and Banking, Volume 29, 1-16.
12. Nelson, C., and C., Plosser, (1982), “Trends and Random Walks in Macroeconomic Time Series,” Journal of Monetary Economics, Volume 10, 139-62.
13. Planning Commission (2012), “Data and Statistics” Ministry of Statistics and Programme Implementation, Government of India.
14. Stock, J., and M., Watson, (1988), “Variable Trends in Economic Time Series,” Journal of Economic Perspectives, Volume 2, 147-74.