Advertisement

Forecasting with the Nonparametric Exclusion-from-Core Inflation Persistence Model Using Real-Time Data

  • Heather L. R. TierneyEmail author
Article

Abstract

This paper contributes to nonparametric forecasting techniques by developing three local nonparametric forecasting methods for the nonparametric exclusion-from-core inflation persistence model that are capable of utilizing revised real-time personal consumption expenditure and core personal consumption expenditure for 62 vintages. Local nonparametric forecasting provides forecasters with a way of parsing the data by permitting a low inflation measure to be included in other low inflationary time periods and vice versa. Furthermore, when examining real-time data, policy-makers can use the nonparametric models to help identify outliers and potential abnormal economic events and problems with the data such as an underlying change in volatility. The most efficient nonparametric forecasting method is the third model, which uses the flexibility of nonparametrics by making forecasts conditional on the forecasted value, which can be used for counterfactual analysis.

Keywords

Inflation persistence Real-time data Monetary policy Nonparametrics Forecasting 

JEL Classification

E52 C14 C53 

Notes

Acknowledgements

I would like to thank in alphabetical order the following people for their gracious comments: Marcelle Chauvet, Graham Elliott, James Hamilton, Hedayeh Samavati, Andres Santos, Zeynep Senyuz, Jack Strauss, Allan Timmermann, and Emre Yoldas, and last but not least, the participants of the 19th Annual Symposium of the Society for Nonlinear Dynamics and Econometrics (2011), the Southern Economic Association (SEA) Meeting 2011, Lafayette College Economics Seminar Series, and the University of California San Diego (UCSD) Econometrics Seminar Series (2013). I also give a very special thanks to Dean Croushore for graciously sharing his knowledge of real-time data with me.

Supplementary material

11294_2019_9726_MOESM1_ESM.docx (123 kb)
ESM 1 (DOCX 123 kb)

References

  1. Altman, D. G., & Bland, J. M. (2005). Standard deviations and standard errors. BMJ [British Medical Journal], 331, 903.Google Scholar
  2. Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning. Artificial Intelligence Review, 11(1), 11–73.Google Scholar
  3. Barkoulas, J. T., Baum, C. F., & Onochie, J. (1997). Nonparametric investigation of the 90-day T-bill rate. Review of Financial Economics, 6(2), 187–198.Google Scholar
  4. Bryan, M. F., & Cecchetti, S. G. (1994). Measuring core inflation. In N. Gregory Mankiw (Ed.), Monetary policy (pp. 195–215). Chicago: University of Chicago Press.Google Scholar
  5. Cai, Z. (2007). Trending time-varying coefficient time series models with serially correlated errors. Journal of Econometrics, 136(1), 163–188.Google Scholar
  6. Cai, Z., & Chen, R. (2006). Flexible seasonal time series models. In B. T. Fomby & D. Terrell (Eds.), Advances in econometrics volume honoring Engle and Granger (pp. 63–87). Orlando: Elsevier.Google Scholar
  7. Cai, Z., Fan, J., & Yao, Q. (2000). Functional-coefficient regression models for nonlinear time series. Journal of the American Statistical Association, 95(451), 941–956.Google Scholar
  8. Chauvet, M., & Tierney, H.L.R. (2009). Real-time changes in monetary policy. Working Paper, Available at: www.faculty.ucr.edu/~chauvet/rtchanges.pdf.
  9. Clark, T. E. (2001). Comparing measures of Core inflation. Federal Reserve Bank of Kansas City Economic Review, 86(2), 5–31.Google Scholar
  10. Coogley, T. (2002). A simple adaptive measure of core inflation. Journal of Money, Credit, and Banking, 43(1), 94–113.Google Scholar
  11. Croushore, D. (2008). Revisions to PCE inflation measures: Implications for monetary policy. Federal Reserve Bank of Philadelphia Working Paper, available at: https://www.philadelphiafed.org/research-and-data/real-time-center/research.
  12. Croushore, D., & Stark, T. (2001). A real-time data set for macroeconomists. Journal of Econometrics, 105(1), 111–130.Google Scholar
  13. Croushore, D., & Stark, T. (2003). A real-time data set for macroeconomists: does the data vintage matter? The Review of Economics and Statistics, 8(3), 605–617.Google Scholar
  14. De Brabanter, K., De Brabanter, J., Gijbels, I., & De Moor, B. (2013). Derivative estimation with local polynomial fitting. Journal of Machine Learning Research, 14(1), 281–301.Google Scholar
  15. Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.Google Scholar
  16. Diebold, F. X., & Nason, J. A. (1990). Nonparametric exchange rate prediction. Journal of International Economics, 28(3–4), 315–332.Google Scholar
  17. Elliott, G. (2002). Comments on 'forecasting with a real-time data set for macroeconomists'. Journal of Macroeconomics, 24(4), 533–539.Google Scholar
  18. Elliott, G., Rothenberg, T., & Stock, J. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836.Google Scholar
  19. Fan, J., & Gijbels, I. (1995). Data-driven selection in polynomial fitting: variable bandwidth and spatial adaptation. Journal of the Royal Statistical Society: Series B, 57(2), 371–394.Google Scholar
  20. Fan, J., & Yao, Q. (1998). Efficient estimation of conditional variance functions in stochastic regressions. Biometrika, 85(3), 645–660.Google Scholar
  21. Federal Reserve of Philadelphia Real-Time Data Research Center. (2018a). Core Price Index for Personal Consumption Expenditure (PCONX). https://www.philadelphiafed.org/research-and-data/real-time-center/real-time-data/data-files/pconx. Accessed 5 Aug 2011.
  22. Federal Reserve of Philadelphia Real-Time Data Research Center. (2018b). Price Index for Personal Consumption Expenditures, Constructed (PCON). https://www.philadelphiafed.org/research-and-data/real-time-center/real-time-data/data-files/pcon. Accessed 5 Aug 2011.
  23. Fujiwara, I., & Koga, M. (2004). A statistical forecasting method for inflation forecasting: Hitting every vector autoregression and forecasting under model uncertainty. Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, 22(1), 123–142.Google Scholar
  24. Gooijer, J. G. D., & Gannoun, A. (1999). Nonparametric conditional predictive regions for time series. Computational Statistics & Data Analysis, 33(3), 259–275.Google Scholar
  25. Gooijer, J. G. D., & Zerom, D. (2000). Kernel-based multistep-ahead predictions of the US short-term interest rate. Journal of Forecasting, 19(4), 335–353.Google Scholar
  26. Hansen, B. E. (2001). GAUSS program for testing for structural change. Available at http://www.ssc.wisc.edu/_bhansen/progs/jep_01.htm. Accessed 18 Oct 2011.
  27. Härdle, W., & Tsybakov, A. (1997). Local polynomial estimator of the volatility function in nonparametric autoregression. Journal of Econometrics, 81(1), 223–242.Google Scholar
  28. Harvey, D. I., Leybourne, S. J., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291.Google Scholar
  29. Harvey, D. I., Leybourne, S. J., & Newbold, P. (1998). Tests for forecast encompassing. Journal of Business & Economic Statistics, 16(2), 254–259.Google Scholar
  30. Johnson, M. (1999). Core inflation: A measure of inflation for policy purposes. Proceedings from Measures of Underlying Inflation and their Role in Conduct of Monetary Policy-Workshop of Central Model Builders at Bank for International Settlements, February.Google Scholar
  31. Lafléche, T., & Armour, J. (2006). Evaluating measures of core inflation. Bank of Canada Review, (Summer) 19–29.Google Scholar
  32. Marron, J. S. (1988). Automatic smoothing parameter selection: A survey. Empirical Economics, 13(3–4), 187–208.Google Scholar
  33. Matzner-Løfber, E., Gannoun, A., & Gooijer, J. G. D. (1998). Nonparametric forecasting: a comparison of three kernel-based methods. Communications in Statistics-Theory and Methods, 27(7), 1532–1617.Google Scholar
  34. Nordhaus, W. D. (2011). The economics of tail events with an application to climate change. Review of Environmental Economics and Policy, 5(2), 240–257.Google Scholar
  35. Pagan, A., & Ullah, A. (1999). Nonparametric econometrics (pp. 118–122). Cambridge: Cambridge University Press.Google Scholar
  36. Pindyck, S. R., & Rubinfeld, L. D. (1998). Econometric models and economic forecasts. Irwin/McGraw-Hill, New York.Google Scholar
  37. Rich, R., & Steindel, C. (2005). A review of core inflation and an evaluation of its measures. Federal Reserve Bank of New York Staff Report No. 236. https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr236.pdf.
  38. Ruppert, D., & Wand, M. P. (1994). Multivariate locally weighted least squares regression. The Annals of Statistics, 22(3), 1346–1370.Google Scholar
  39. Tierney, H. L. R. (2011). Real-time data revisions and the PCE measure of inflation. Economic Modelling, 28(4), 1763–1773.Google Scholar
  40. Tierney, H. L. R. (2012). Examining the ability of core inflation to capture the overall trend of Total inflation. Applied Economics, 44(4), 493–514.Google Scholar
  41. Tierney, H. L. R. (2018). Tracking real-time data revisions in inflation persistence. Applied Economics, 1–23.  https://doi.org/10.1080/00036846.2018.1540849.
  42. Vilar-Fernández, J. M., & Cao, R. (2007). Nonparametric forecasting in time series: a comparative study. Communications in Statistics: Simulation and Computation, 36(2), 311–334.Google Scholar
  43. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.Google Scholar

Copyright information

© International Atlantic Economic Society 2019

Authors and Affiliations

  1. 1.Economics Department, Doermer School of BusinessPurdue University Fort WayneFort WayneUSA

Personalised recommendations