Do the Indian Agricultural Commodities’ Prices Exhibit Non-Linear Mean Reversion? An Empirical Evidence

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DOI: 10.4236/tel.2015.52039    1,829 Downloads   2,120 Views  

ABSTRACT

Indian economy’s inflation index often reflects double digit tendencies due to supply side shortages caused by droughts, rise in the prices of crude oil in the international markets etc. These factors may be responsible for non-linear behaviour of inflation index. Against this backdrop, an attempt is made in this study to capture non-linear mean reversion of prices of 47 agricultural commodities of India. The study employs powerful non-linear unit root test so as to generate robust findings to infer valid policy implications. The results of the study indicate the presence of unit root with drift process for Food Grains, Cereals, Pulses, Fruits, Vegetables, Primary Articles, Ragi and Rice. And for rest of the commodities, it is observed that there is evidence of mean reversion and therefore, the impact would be only temporary in nature. Thus, the empirical inferences enable the policy makers to design appropriate short term and long term polices related to the prices of agricultural commodities.

Cite this paper

Tiwari, A. , Aruna, M. and Dash, A. (2015) Do the Indian Agricultural Commodities’ Prices Exhibit Non-Linear Mean Reversion? An Empirical Evidence. Theoretical Economics Letters, 5, 332-342. doi: 10.4236/tel.2015.52039.

References

[1] Gil-Alana, L.A. and Tripathy, T. (2014) Mean Reversion in Agricultural Commodity Prices in India. International Advanced Economic Research, 20, 385-398.
[2] Rahman, A. and Samir, S. (2008) Random Walk and Breaking Trend in Financial Series: An Econometric Critique of Unit Root Tests. Review of Financial Economics, 17, 204-212. http://dx.doi.org/10.1016/j.rfe.2007.05.002
[3] Brock, W.A., Dechert, W.D. and Scheinkman, J.A. (1987) A Test for Independence Based on the Correlation Dimension, Department of Economics. University of Wisconsin at Madison, University of Houston, and University of Chicago.
[4] Brock, W.A., Scheinkman, J.A., Dechert, W.D. and LeBaron, B. (1996) A Test for Independence Based on the Correlation Dimension. Econometric Reviews, 15, 197-235. http://dx.doi.org/10.1080/07474939608800353
[5] Grassberger, P. and Procaccia, I. (1983) Measuring the Strangeness of Strange Attractors. Physica D (Nonlinear Phenomena), 9, 189-208.
[6] Kapetanios, G., Shin, Y. and Snell, A. (2003) Testing for a Unit Root in the Nonlinear STAR Framework. Journal of Econometrics, 112, 359-379. http://dx.doi.org/10.1016/S0304-4076(02)00202-6
[7] Campbell, J.Y. and Perron, P. (1991) Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots. NBER Macroeconomics Annual, 6, 141-201. http://dx.doi.org/10.2307/3585053
[8] Lothian, J.R. and Taylor, M.P. (1996) Real Exchange Rate Behaviour: The Recent Float from the Perspective of the Past Two Centuries. Journal of Political Economy, 104, 488-509. http://dx.doi.org/10.1086/262031
[9] Lothian, J.R. and Taylor, M.P. (1997) Real Exchange Rate Behavior. Journal of International Money and Finance, 16, 945-954. http://dx.doi.org/10.1016/S0261-5606(97)00014-4
[10] Cuddington, J.T. and Hong, L. (2000) Purchasing Power Parity over Two Centuries? Journal of International Money and Finance, 19, 753-757. http://dx.doi.org/10.1016/S0261-5606(00)00029-2
[11] Hall, A.D. (1994) Testing for a Unit Root in Time Series with Pre-Test Data Based Model Selection. Journal of Business and Economic Statistics, 12, 461-470.
[12] Breitung, J. (2002) Nonparametric Tests for Unit Roots and Cointegration. Journal of Econometrics, 108, 343-363. http://dx.doi.org/10.1016/S0304-4076(01)00139-7
[13] Bierens, H.J. and Guo, S. (1993) Testing Stationarity and Trend Stationarity against the Unit Root Hypothesis. Econometric Reviews, 12, 1-32. http://dx.doi.org/10.1080/07474939308800252
[14] Ng, S. and Perron, P. (1995) Unit Root Tests in ARMA Models with Data Dependent Methods for the Selection of the Truncation Lag. Journal of the American Statistical Association, 90, 268-281. http://dx.doi.org/10.1080/01621459.1995.10476510

  
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