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On the Granger causality between median inflation and price dispersion

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  • Richard Ashley
  • Haichun Ye

Abstract

The Granger-causal relationship between the size and dispersion of fluctuations in sub-components of the US Consumer Price Index (CPI) is examined using both in-sample and out-of-sample tests and data from January 1968 to December 2008. Strong in-sample evidence is found for feedback between median inflation and price dispersion; the evidence for Granger-causation from median inflation to price dispersion remains strong in out-of-sample testing, but is less strong for Granger-causation in the opposite direction. The implications of these results for the variety of price-level determination models in the literature are discussed.

Suggested Citation

  • Richard Ashley & Haichun Ye, 2012. "On the Granger causality between median inflation and price dispersion," Applied Economics, Taylor & Francis Journals, vol. 44(32), pages 4221-4238, November.
  • Handle: RePEc:taf:applec:44:y:2012:i:32:p:4221-4238 DOI: 10.1080/00036846.2011.587788
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    1. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    2. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.
    3. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    4. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    5. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    6. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
    7. Richard De Abreu Lourenco & David Gruen, 1995. "Price Stickiness and Inflation," RBA Research Discussion Papers rdp9502, Reserve Bank of Australia.
    8. Brian Peterson & Shouyong Shi, 2004. "Money, price dispersion and welfare," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 24(4), pages 907-932, November.
    9. Todd Clark & Michael McCracken, 2005. "Evaluating Direct Multistep Forecasts," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 369-404.
    10. Parsley, David C, 1996. "Inflation and Relative Price Variability in the Short and Long Run: New Evidence from the United States," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 28(3), pages 323-341, August.
    11. Eytan Sheshinski & Yoram Weiss, 1977. "Inflation and Costs of Price Adjustment," Review of Economic Studies, Oxford University Press, vol. 44(2), pages 287-303.
    12. Eytan Sheshinski & Yoram Weiss, 1983. "Optimum Pricing Policy under Stochastic Inflation," Review of Economic Studies, Oxford University Press, vol. 50(3), pages 513-529.
    13. Ashley, Richard, 1981. "Inflation and the Distribution of Price Changes across Markets: A Causal Analysis," Economic Inquiry, Western Economic Association International, vol. 19(4), pages 650-660, October.
    14. Ashley, Richard, 1998. "A new technique for postsample model selection and validation," Journal of Economic Dynamics and Control, Elsevier, vol. 22(5), pages 647-665, May.
    15. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    16. Allen Head & Alok Kumar, 2005. "Price Dispersion, Inflation, And Welfare," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(2), pages 533-572, May.
    17. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    18. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
    19. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    20. Debelle, Guy & Lamont, Owen, 1997. "Relative Price Variability and Inflation: Evidence from U.S. Cities," Journal of Political Economy, University of Chicago Press, vol. 105(1), pages 132-152, February.
    21. Grier, Kevin B. & Perry, Mark J., 1996. "Inflation, inflation uncertainty, and relative price dispersion: Evidence from bivariate GARCH-M models," Journal of Monetary Economics, Elsevier, vol. 38(2), pages 391-405, October.
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    Cited by:

    1. Batten, Jonathan A. & Ciner, Cetin & Kosedag, Arman & Lucey, Brian M., 2017. "Is the price of gold to gold mining stocks asymmetric?," Economic Modelling, Elsevier, vol. 60(C), pages 402-407.
    2. Ye, Haichun & Ashley, Richard & Guerard, John, 2015. "Comparing the effectiveness of traditional vs. mechanized identification methods in post-sample forecasting for a macroeconomic Granger causality analysis," International Journal of Forecasting, Elsevier, vol. 31(2), pages 488-500.
    3. Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 1-20, March.
    4. Richard A. Ashley & Christopher F. Parmeter, 2013. "Sensitivity Analysis of Inference in GMM Estimation With Possibly-Flawed Moment Conditions," Working Papers e07-40, Virginia Polytechnic Institute and State University, Department of Economics.

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