The Evasive Predictive Ability of Core Inflation

Pincheira, Pablo and Selaive, Jorge and Nolazco, Jose Luis (2016): The Evasive Predictive Ability of Core Inflation.

Abstract

We explore the ability of traditional core inflation –consumer prices excluding food and energy– to predict headline CPI annual inflation. We analyze a sample of OECD and non-OECD economies using monthly data from January 1994 to March 2015. Our results indicate that sizable predictability emerges for a small subset of countries. For the rest of our economies predictability is either subtle or undetectable. These results hold true even when implementing an out-of-sample test of Granger causality especially designed to compare forecasts from nested models. Our findings partially challenge the common wisdom about the ability of core inflation to forecast headline inflation, and suggest a careful weighting of the traditional exclusion of food and energy prices when assessing the size of the monetary stimulus.

• Item Type: MPRA Paper
• Language: English
• Keywords: Inflation, Forecasting, Time Series, Monetary Policy, Core Inflation
• Subjects: E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E43 - Interest Rates: Determination, Term Structure, and Effects; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy

References

1. ATKESON A. and L. OHANIAN (2001). “Are Phillips Curves Useful for Forecasting Inflation” Federal Reserve Bank of Minneapolis, Quarterly Review 25 (1), pp. 2-11.

2. BLANCHARD O., G. DELL’ARICCIA and P. MAURO (2010). “Rethinking Macroeconomic Policy” Journal of Money, Credit and Banking, Supplement to Vol. 42, N° 6 pp. 199-215.

3. BERMINGHAM C. (2007). “How Useful is Core Inflation for Forecasting Headline Inflation?” The Economic and Social Review, Vol. 38, N° 3, Winter, pp. 355-377.

4. BLINDER, A S., and R. REIS. (2005). “Understanding the Greenspan Standard,” Prepared for the Federal Reserve Bank of Kansas City symposium, The Greenspan Era: Lessons for the Future, Jackson Hole, Wyoming, August 25-27, 2005.

5. BOX, G., G. JENKINS and G. REINSEL (2008). “Time Series Analysis: Forecasting and Control”. 4th edition, Wiley.

6. BULLARD, J. (2011a). “Measuring Inflation: The Core is Rotten". Federal Reserve Bank of St. Louis Review, July/August 2011, 93(4), pp. 223-33.

7. BULLARD, J. (2011b). “President's Message: Headline vs. Core Inflation: A Look at Some Issues". Federal Reserve Bank of St. Louis.

8. CICCARELLI, M. and B. MOJON, (2010). “Global Inflation.” TheReview of Economicsand Statistics 92(3), pp. 524-535.

9. CLARK, T. and K. WEST (2006). “Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis.” Journal of Econometrics 135(1-2), pp. 155-186

10. CLARK, T. and K. WEST (2007). “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models.” Journal of Econometrics 138, pp. 291-311.

11. CLARK, T. and M. McCRACKEN (2001). “Tests of equal forecast accuracy and encompassing for nested models”. Journal of Econometrics 105, 85–110.

12. CLARK, T. and M. McCRACKEN (2005). “Evaluating direct multistep forecasts”. Econometric Reviews 24, 369–404.

13. CLARK, T. (2001). “Comparing Measures of Core Inflation”. Federal Reserve Bank of Kansas City Economic Review. Second Quarter 2001. pp. 5-31.

14. CLEMENTS, M. and D. HENDRY (2001). “Forecasting with difference-stationary and trend-stationary models”. Econometrics Journal (4), pp. S1-S19.

15. COGLEY, T. (2002) “A Simple Adaptive Measure of Core Inflation.” Journal of Money, Credit, and Banking, 34, 94–113.

16. CRONE T., N. KHETTRY, L. MESTER and J. NOVAK (2013). “Core Measures of Inflation as Predictors of Total Inflation”. Journal of Money, Credit and Banking Vol. 45, N° 2-3, pp. 505-519.

17. DIEBOLD, F. and R. MARIANO (1995). “Comparing Predictive Accuracy”. Journal of Business and Economic Statistics 13(3). pp. 253-263.

18. FAUST, J. and J. WRIGHT (2013). “Forecasting Inflation”. Chapter 1 in Handbook of Economic Forecasting Vol. 2. pp. 2-56.

19. FREEMAN, D. (1998). “Do core inflation measures help forecast inflation?” Economics Letters 58, 143–147.

20. KHETTRY N. and L. MESTER (2006). “Core Inflation as a Predictor of Total Inflation”. Research Rap-Special Report, Research Department, Federal Reserve Bank of Philadelphia. April, 2006.

21. KILEY, M. (2008) “Estimating the Common Trend Rate of Inflation for Consumer Prices and Consumer Prices Excluding Food and Energy Prices.” Finance and Economics Discussion Series Paper 2008-38, Board of Governors of the Federal Reserve System.

22. LE BIHAN H. and F. SÉDILLOT (2000). “Do core inflation measures help forecast inflation? Out-of-sample evidence from French data” Economics Letters 69 (2000) 261–266.

23. LEI LEI SONG (2005), “Do underlying measures of inflation outperform headline rates? Evidence from Australian Data. Applied Economics, Taylor & Francis Journals, vol. 37(3), pp. 339-345.

24. MARCELLINO, M., J. STOCK and M. WATSON (2006). “A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series,” Journal of Econometrics 127 (1-2): 499-526.

25. MEYER, B. and M. PASAOGULLARI. (2010) “Simple Ways to Forecast Inflation: What Works Best?” Economic Commentary No. 2010–17, Federal Reserve Bank of Cleveland.

26. NEWEY, W.K. and K. WEST (1987). “A Simple, Positive, Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica 55 (3): 703-8

27. PINCHEIRA, P. and C.A. MEDEL (2012a). “Forecasting Inflation with a Random Walk,” Working Paper N°669 Central Bank of Chile.

28. PINCHEIRA, P. and C.A. MEDEL (2015). “Forecasting Inflation with a Simple and Accurate Benchmark: The Case of the US and a Set of Inflation Targeting Countries”. Finance a uvěr-Czech Journal of Economics and Finance, 65, 2015, no. 1.

29. PINCHEIRA, P. and. A. GATTY (2015). “Forecasting Chilean Inflation with International Factors” Empirical Economics, forthcoming.

30. RICH, R. and C. STEINDEL (2007). “A Comparison of Measures of Core Inflation”. FRBNY Economic Policy Review. December.

31. ROBALO C., P. DUARTE and L. MORAIS (2003). “Evaluating core inflation indicators”. Economic Modelling 20 (2003) pp. 765-775.

32. SMITH, J. K. (2012) “PCE Inflation and Core Inflation.” Working Paper 1203, Federal Reserve Bank of Dallas, January 2010.

33. STOCK J. and M. WATSON (1999). “Forecasting Inflation”. Journal of Monetary Economics 44, pp. 293-335.

34. STOCK J. and M. WATSON (2002). “Macroeconomic forecasting using diffusion indexes”. Journal of Business and Economic Statistics 20, pp. 147-162.

35. STOCK J. and M. WATSON (2008). “Phillips Curve Inflation Forecasts”. NBER Working Papers 14322. National Bureau of Economic Research, Inc.

36. STOCK J. and M. WATSON (2015). “Core Inflation and Trend Inflation.” NBER Working Paper N°21282

37. WEST, K. (1996), “Asymptotic Inference about Predictive Ability” Econometrica 64(5) pp.1067-84.