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Causality and error correction in Markov chain: Inflation in India revisited

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  • N. Vijayamohanan Pillai

    (Centre for Development Studies)

Abstract

The present paper proposes certain statistical tests, both conceptually simple and computationally easy, for analysing state-specific prima facie probabilistic causality and error correction mechanism in the context of a Markov chain of time series data arranged in a contingency table of present versus previous states. It thus shows that error correction necessarily follows causality (that is temporal dependence) or vice versa, suggesting apparently that the two represent the same aspect! The result is applied to an analysis of inflation in India during the last three decades separately and also together based on the monthly general price level (WPI - all commodities) and 23 constituent groups/items, as well as on the three consumer price index (CPI) numbers.

Suggested Citation

  • N. Vijayamohanan Pillai, 2004. "Causality and error correction in Markov chain: Inflation in India revisited," Centre for Development Studies, Trivendrum Working Papers 366, Centre for Development Studies, Trivendrum, India.
  • Handle: RePEc:ind:cdswpp:366
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    References listed on IDEAS

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    1. McQueen, Grant & Thorley, Steven, 1991. "Are Stock Returns Predictable? A Test Using Markov Chains," Journal of Finance, American Finance Association, vol. 46(1), pages 239-263, March.
    2. Masson, Paul R., 2001. "Exchange rate regime transitions," Journal of Development Economics, Elsevier, vol. 64(2), pages 571-586, April.
    3. Krenz, Ronald D., 1964. "Projection of Farm Numbers for North Dakota With Markov Chains," Journal of Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, vol. 16(3), pages 1-7, July.
    4. Tsiang, S C, 1978. "The Diffusion of Reserves and the Money Supply Multiplier," Economic Journal, Royal Economic Society, vol. 88(350), pages 269-284, June.
    5. Paul Cheshire & Stefano Magrini, 2000. "Endogenous Processes in European Regional Growth: Convergence and Policy," Growth and Change, Wiley Blackwell, vol. 31(4), pages 455-479.
    6. McMillen, Daniel P. & McDonald, John F., 1991. "A Markov Chain model of zoning change," Journal of Urban Economics, Elsevier, vol. 30(2), pages 257-270, September.
    7. Jin-Chuan Duan & Technology & Jean-Guy Simonato, "undated". "American GARCH Option Pricing by a Markov Chain Approximation," Computing in Economics and Finance 1997 131, Society for Computational Economics.
    8. James J. Solberg, 1975. "A Graph Theoretic Formula for the Steady State Distribution of Finite Markov Processes," Management Science, INFORMS, vol. 21(9), pages 1040-1048, May.
    9. Don Webber, 2001. "Convergence of labour's factor reward between regions of the EU," Applied Economics Letters, Taylor & Francis Journals, vol. 8(5), pages 355-357.
    10. Temel, Tugrul T. & Tansel, Aysit & Albersen, P.J., 1999. "Convergence and Spatial Patterns in Labor Productivity: Nonparametric Estimations for Turkey," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 29(1), pages 1-17.
    11. Andras Brody, 2000. "The Monetary Multiplier," Economic Systems Research, Taylor & Francis Journals, vol. 12(2), pages 215-219.
    12. Duan, Jin-Chuan & Simonato, Jean-Guy, 2001. "American option pricing under GARCH by a Markov chain approximation," Journal of Economic Dynamics and Control, Elsevier, vol. 25(11), pages 1689-1718, November.
    13. Kawagoe, Masaaki, 1999. "Regional Dynamics in Japan: A Reexamination of Barro Regressions," Journal of the Japanese and International Economies, Elsevier, vol. 13(1), pages 61-72, March.
    14. Vijayamohanan Pillai N, 2002. "A Markov Chain Model of Inflation in India," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 37(1), pages 91-116, January.
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    More about this item

    Keywords

    Markov chain; Steady state probability; India; Inflation; Return period;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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