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Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States

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Author Info

  • Richard G. Anderson
  • Jane M. Binner
  • Vincent A. Schmidt

Abstract

This paper examines the inflation "pass-through" problem in American monetary policy, defined as the relationship between changes in the growth rates of individual goods and the subsequent economy-wide rate of growth of consumer prices. Granger causality tests robust to structural breaks are used to establish initial relationships. Then, feedforward artificial neural network (ANN) is used to approximate the functional relationship between selected component subindexes and the headline CPI. Moving beyond the ANN “black box,” we illustrate how decision rules can be extracted from the network. Our custom decompositional extraction algorithm generates rules in humanreadable and machine-executable form (Matlab code). Our procedure provides an additional route, beyond direct Bayesian estimation, for empirical econometric relationships to be embedded in DSGE models. A topic for further research is embedding decision rules within such models.

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Bibliographic Info

Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2011-007.

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Date of creation: 2011
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Handle: RePEc:fip:fedlwp:2011-007

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Keywords: Inflation (Finance) ; Consumer price indexes;

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References

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  1. Ben S. Bernanke & Mark Gertler & Mark Watson, 1997. "Systematic Monetary Policy and the Effects of Oil Price Shocks," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 28(1), pages 91-157.
  2. Yu-chin Chen & Kenneth Rogoff & Barbara Rossi, 2010. "Can Exchange Rates Forecast Commodity Prices?," Working Papers 10-07, Duke University, Department of Economics.
  3. Richard G. Anderson & Jane M. Binner & Vincent A. Schmidt, 2011. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Working Papers 2011-007, Federal Reserve Bank of St. Louis.
  4. Chen, Shiu-Sheng, 2009. "Oil price pass-through into inflation," Energy Economics, Elsevier, vol. 31(1), pages 126-133, January.
  5. José De Gregorio & Oscar Landerretche & Christopher Neilson, 2007. "Another Pass-Through Bites The Dust? Oil Prices And Inflation," Working Papers wp238, University of Chile, Department of Economics.
  6. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  7. Todd E. Clark & Stephen J. Terry, 2009. "Time variation in the inflation passthrough of energy prices," Research Working Paper RWP 09-06, Federal Reserve Bank of Kansas City.
  8. Hooker, Mark A, 2002. "Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications versus Changes in Regime," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 540-61, May.
  9. Rossi, Barbara, 2002. "Optimal Tests for Nested Model Selection with Underlying Parameter Instability," Working Papers 02-05, Duke University, Department of Economics.
  10. Paul van den Noord & Christophe André, 2007. "Why has Core Inflation Remained so Muted in the Face of the Oil Shock?," OECD Economics Department Working Papers 551, OECD Publishing.
  11. Kilian, Lutz, 2007. "The Economic Effects of Energy Price Shocks," CEPR Discussion Papers 6559, C.E.P.R. Discussion Papers.
  12. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Society for Computational Economics, vol. 32(4), pages 383-406, November.
  13. John Geweke & Gianni Amisano, 2007. "Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns," Working Papers 0705, University of Brescia, Department of Economics.
  14. Binner, J.M. & Tino, P. & Tepper, J. & Anderson, R. & Jones, B. & Kendall, G., 2010. "Does money matter in inflation forecasting?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4793-4808.
  15. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
  16. James D. Hamilton, 2000. "What is an Oil Shock?," NBER Working Papers 7755, National Bureau of Economic Research, Inc.
  17. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-75, July.
  18. Herrera, Ana Maria & Hamilton, James D., 2001. "Oil Shocks and Aggregate Macroeconomic Behavior: The Role of Monetary Policy," University of California at San Diego, Economics Working Paper Series qt4qp0p0v5, Department of Economics, UC San Diego.
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Cited by:
  1. Anderson, Richard G. & Binner, Jane M. & Schmidt, Vincent A., 2012. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Economics Letters, Elsevier, vol. 117(1), pages 174-177.

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