IDEAS home Printed from https://ideas.repec.org/a/kap/iaecre/v25y2019i1d10.1007_s11294-019-09726-7.html
   My bibliography  Save this article

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

Author

Listed:
  • Heather L. R. Tierney

    (Purdue University Fort Wayne)

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.

Suggested Citation

  • Heather L. R. Tierney, 2019. "Forecasting with the Nonparametric Exclusion-from-Core Inflation Persistence Model Using Real-Time Data," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(1), pages 39-63, February.
  • Handle: RePEc:kap:iaecre:v:25:y:2019:i:1:d:10.1007_s11294-019-09726-7
    DOI: 10.1007/s11294-019-09726-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11294-019-09726-7
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11294-019-09726-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tierney, Heather L.R., 2011. "Real-time data revisions and the PCE measure of inflation," Economic Modelling, Elsevier, vol. 28(4), pages 1763-1773, July.
    2. Elliott, Graham, 2002. "Comments on 'Forecasting with a real-time data set for macroeconomists'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 533-539, December.
    3. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
    4. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    5. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    6. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    7. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    8. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    9. William D. Nordhaus, 2011. "The Economics of Tail Events with an Application to Climate Change," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 5(2), pages 240-257, Summer.
    10. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Hardle, W. & Tsybakov, A., 1997. "Local polynomial estimators of the volatility function in nonparametric autoregression," Journal of Econometrics, Elsevier, vol. 81(1), pages 223-242, November.
    13. Heather L. R. Tierney, 2012. "Examining the ability of core inflation to capture the overall trend of total inflation," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 493-514, February.
    14. Marron, J S, 1988. "Automatic Smoothing Parameter Selection: A Survey," Empirical Economics, Springer, vol. 13(3/4), pages 187-208.
    15. John T. Barkoulas & Christopher F. Baum & Joseph Onochie, 1997. "A nonparametric investigation of the 90‐day t‐bill rate," Review of Financial Economics, John Wiley & Sons, vol. 6(2), pages 187-198.
    16. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
    17. Todd E. Clark, 2001. "Comparing measures of core inflation," Economic Review, Federal Reserve Bank of Kansas City, vol. 86(Q II), pages 5-31.
    18. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    19. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    20. Fujiwara, Ippei & Koga, Maiko, 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, vol. 22(1), pages 123-142, March.
    21. Robert W. Rich & Charles Steindel, 2005. "A review of core inflation and an evaluation of its measures," Staff Reports 236, Federal Reserve Bank of New York.
    22. Fan, Jianqing & Yao, Qiwei, 1998. "Efficient estimation of conditional variance functions in stochastic regression," LSE Research Online Documents on Economics 6635, London School of Economics and Political Science, LSE Library.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tierney, Heather L.R., 2011. "Forecasting and tracking real-time data revisions in inflation persistence," MPRA Paper 34439, University Library of Munich, Germany.
    2. Tierney, Heather L.R., 2011. "Real-time data revisions and the PCE measure of inflation," Economic Modelling, Elsevier, vol. 28(4), pages 1763-1773, July.
    3. Heather L. R. Tierney, 2012. "Examining the ability of core inflation to capture the overall trend of total inflation," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 493-514, February.
    4. Tierney, Heather L.R., 2009. "Evaluating Exclusion-from-Core Measures of Inflation using Real-Time Data," MPRA Paper 17856, University Library of Munich, Germany.
    5. Tierney, Heather L.R., 2009. "A Local Examination for Persistence in Exclusions-from-Core Measures of Inflation Using Real-Time Data," MPRA Paper 13089, University Library of Munich, Germany.
    6. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    7. Aslanidis, Nektarios & Cipollini, Andrea, 2010. "Leading indicator properties of US high-yield credit spreads," Journal of Macroeconomics, Elsevier, vol. 32(1), pages 145-156, March.
    8. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
    9. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    10. Harvey, David I. & Newbold, Paul, 2003. "The non-normality of some macroeconomic forecast errors," International Journal of Forecasting, Elsevier, vol. 19(4), pages 635-653.
    11. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    12. Croushore Dean, 2010. "An Evaluation of Inflation Forecasts from Surveys Using Real-Time Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, May.
    13. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    14. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    15. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    16. Michael S. Smith & Shaun P. Vahey, 2016. "Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 416-434, July.
    17. Dean Croushore & Katherine Marsten, 2016. "Do GDP Forecasts Respond Efficiently to Changes in Interest Rates?," Working Papers 16-17, Federal Reserve Bank of Philadelphia.
    18. Golinelli, Roberto & Parigi, Giuseppe, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    19. Biswas, Anindya, 2014. "The output gap and expected security returns," Review of Financial Economics, Elsevier, vol. 23(3), pages 131-140.
    20. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.

    More about this item

    Keywords

    Inflation persistence; Real-time data; Monetary policy; Nonparametrics; Forecasting;
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:iaecre:v:25:y:2019:i:1:d:10.1007_s11294-019-09726-7. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.