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Predicting Inflation: Does The Quantity Theory Help?

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  • Lance J. Bachmeier

    ()
    (East Carolina University)

  • Norman R. Swanson

    ()
    (Rutgers University)

Abstract

Various inflation forecasting models are compared using a simulated out-of-sample forecasting framework. We focus on the question of whether monetary aggregates are useful for forecasting inflation, but unlike previous work we examine a wide range of forecast horizons and allow for estimated as well as theoretically specified cointegrating relationships in some of our models. Our findings indicate that there are forecasting gains from allowing monetary aggregates to enter into prediction models via cointegrating restrictions among money, prices, and output derived from a simple version of the quantity theory, but only when the cointegrating relations are specified a priori based on economic theory. When estimated cointegrating relations are used in a vector error correction (VEC) model, a vector autoregression (VAR) model in differences predicts better. These results hold, even during the 1990s, and evidence is presented suggesting that previous findings of a breakdown in the cointegrating relationship among prices, money, and output is the result of a failure of M2 as a measure of the money stock, and is not due to money demand instabilities. Two Monte Carlo experiments that lend credence to our findings are also reported on. The first shows that cointegration vector parameter estimation error is crucial when using VEC models for forecasting, and helps to explain previous findings of the failure of VEC models to forecast better than VAR models. The second shows that random walk and other atheoretical models routinely forecast better than correctly specified alternative models, due to parameter estimation error, indicating that caution needs to be exercised when interpreting the results of such comparisons, particularly when making statements concerning the usefulness of empirical models for use in policy-setting.

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Bibliographic Info

Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 200317.

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Date of creation: 27 Oct 2003
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Handle: RePEc:rut:rutres:200317

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Keywords: Inflation; Phillips curve; Forecast evaluation; cointegration;

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  1. William Barnett, 2005. "Monetary Aggregation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 200510, University of Kansas, Department of Economics, revised Mar 2005.
  2. Ahmed, Shaghil & Ickes, Barry W. & Ping Wang & Byung Sam Yoo, 1993. "International Business Cycles," American Economic Review, American Economic Association, vol. 83(3), pages 335-59, June.
  3. Martin Feldstein & James H. Stock, 1994. "The Use of a Monetary Aggregate to Target Nominal GDP," NBER Chapters, in: Monetary Policy, pages 7-69 National Bureau of Economic Research, Inc.
  4. N. Gregory Mankiw, 2000. "The Inexorable and Mysterious Tradeoff Between Inflation and Unemployment," NBER Working Papers 7884, National Bureau of Economic Research, Inc.
  5. 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.
  6. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  7. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  8. Thomas Sargent & Noah Williams & Tao Zha, 2006. "The conquest of South American inflation," Working Paper 2006-20, Federal Reserve Bank of Atlanta.
  9. Francis X. Diebold & Peter F. Christoffersen, 1997. "Cointegration and Long-Horizon Forecasting," IMF Working Papers 97/61, International Monetary Fund.
  10. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  11. Thomas Laubach & Jeffery D. Amato, 2000. "Forecast-based monetary policy," BIS Working Papers 89, Bank for International Settlements.
  12. Eric M. Leeper & Jennifer E. Roush, 2003. "Putting "M" back in monetary policy," Proceedings, Federal Reserve Bank of Cleveland, pages 1217-1264.
  13. James H. Stock & Martin Feldstein, 1994. "Measuring Money Growth When Financial Markets Are Changing," NBER Working Papers 4888, National Bureau of Economic Research, Inc.
  14. Graham Elliott & Michael Jansson, . "Testing for Unit Roots with Stationary Covariates," Economics Working Papers 2000-6, School of Economics and Management, University of Aarhus.
  15. Arturo Estrella & Frederic S. Mishkin, 1996. "Is There a Role for Monetary Aggregates in the Conduct of Monetary Policy?," NBER Working Papers 5845, National Bureau of Economic Research, Inc.
  16. Culver, Sarah E & Papell, David H, 1997. "Is There a Unit Root in the Inflation Rate? Evidence from Sequential Break and Panel Data Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 435-44, July-Aug..
  17. Swanson, Norman R., 1998. "Money and output viewed through a rolling window," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 455-474, May.
  18. John B. Carlson & Dennis L. Hoffman & Benjamin D. Keen & Robert H. Rasche, 1999. "Results of a study of the stability of cointegrating relations comprised of broad monetary aggregates," Working Paper 9917, Federal Reserve Bank of Cleveland.
  19. Ahmed, Shaghil & Rogers, John H., 2000. "Inflation and the great ratios: Long term evidence from the U.S," Journal of Monetary Economics, Elsevier, vol. 45(1), pages 3-35, February.
  20. Mc Cracken, Michael W., 2000. "Robust out-of-sample inference," Journal of Econometrics, Elsevier, vol. 99(2), pages 195-223, December.
  21. Jordi Gali & Mark Gertler, 2000. "Inflation Dynamics: A Structural Econometric Analysis," NBER Working Papers 7551, National Bureau of Economic Research, Inc.
  22. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
  23. Lin, Jin-Lung & Tsay, Ruey S, 1996. "Co-integration Constraint and Forecasting: An Empirical Examination," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 519-38, Sept.-Oct.
  24. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-94, Sept.-Oct.
  25. Gerlach, Stefan & Svensson, Lars E. O., 2003. "Money and inflation in the euro area: A case for monetary indicators?," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1649-1672, November.
  26. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
  27. Hoffman, Dennis L & Rasche, Robert H, 1996. "Assessing Forecast Performance in a Cointegrated System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 495-517, Sept.-Oct.
  28. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  29. Clark, Todd E. & McCracken, Michael W., 2006. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1127-1148, August.
  30. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January.
  31. Dean Croushore, 1998. "Evaluating inflation forecasts," Working Papers 98-14, Federal Reserve Bank of Philadelphia.
  32. William Barnett & Apostolos Serletis & W. Erwin Diewert, 2005. "The Theory of Monetary Aggregation (book front matter)," Macroeconomics 0511008, EconWPA.
  33. David H. Romer & Christina D. Romer, 2000. "Federal Reserve Information and the Behavior of Interest Rates," American Economic Review, American Economic Association, vol. 90(3), pages 429-457, June.
  34. Ben S. Bernanke & Jean Boivin, 2001. "Monetary Policy in a Data-Rich Environment," NBER Working Papers 8379, National Bureau of Economic Research, Inc.
  35. Blinder, Alan S, 1997. "Is There a Core of Practical Macroeconomics That We Should All Believe?," American Economic Review, American Economic Association, vol. 87(2), pages 240-43, May.
  36. Ng, S. & Perron, P., 1994. "Unit Root Tests ARMA Models with Data Dependent Methods for the Selection of the Truncation Lag," Cahiers de recherche 9423, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  37. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  38. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  39. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
  40. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
  41. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  42. Stock, James & Feldstein, Martin, 1996. "Measuring Money Growth When Financial Markets are Changing," Scholarly Articles 2799053, Harvard University Department of Economics.
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Citations

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Cited by:
  1. Pär Österholm & Helge Berger, 2008. "Does Money Matter for U.S. Inflation? Evidence from Bayesian VARs," IMF Working Papers 08/76, International Monetary Fund.
  2. Anthony Garratt & Gary Koop & Emi Mise & Shaun P Vahey, 2007. "Real-time Prediction with UK Monetary Aggregates in the Presence of Model Uncertainty," Birkbeck Working Papers in Economics and Finance 0714, Birkbeck, Department of Economics, Mathematics & Statistics.
  3. Ratti, Ronald A & Vespignani, Joaquin L., 2013. "Commodity Prices and BRIC and G3 Liquidity: A SFAVEC Approach," Working Papers 17096, University of Tasmania, School of Economics and Finance, revised 09 Jan 2013.
  4. Helge Berger & Pär Österholm, 2011. "Does Money Growth Granger Cause Inflation in the Euro Area? Evidence from Out‐of‐Sample Forecasts Using Bayesian VARs," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 45-60, March.
  5. Roman Horvath & Lubos Komarek & Filip Rozsypal, 2010. "Does Money Help Predict Inflation? An Empirical Assessment for Central Europe," Working Papers 2010/05, Czech National Bank, Research Department.
  6. Joao Miguel Sousa & Andrea Zaghini, 2007. "Global Monetary Policy Shocks in the G5: a SVAR Approach," CEIS Research Paper 89, Tor Vergata University, CEIS.
  7. Ronald A. Ratti & Joaquin L. Vespignani, 2014. "Not all international monetary shocks are alike for the Japanese economy," CAMA Working Papers 2014-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  8. Michael Graff, 2008. "The Quantity Theory of Money in Historical Perspective," KOF Working papers 08-196, KOF Swiss Economic Institute, ETH Zurich.
  9. Ronald A. Ratti & Joaquin L. Vespignani, 2014. "Oil prices and the economy: A global perspective," CAMA Working Papers 2014-41, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  10. Berger, Helge & Österholm, Pär, 2008. "Does money growth granger-cause inflation in the Euro Area? Evidence from output-of-sample forecasts using Bayesian VARs," Discussion Papers 2008/10, Free University Berlin, School of Business & Economics.
  11. Arto Kovanen, 2011. "Does Money Matter for Inflation in Ghana?," IMF Working Papers 11/274, International Monetary Fund.
  12. Doyle, Matthew, 2006. "Empirical Phillips Curves in OECD Countries: Has There Been A Common Breakdown?," Staff General Research Papers 12684, Iowa State University, Department of Economics.

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