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

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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.

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Notes

  1. This paper establishes the local nonparametric techniques used in Tierney (2018), which went on to explore in depth the effect of vintage when it comes to forecasting inflation persistence.

  2. The vintages for PCE and Core PCE begin in V_1965:Q4 and V_1996:Q1. It should be noted that benchmark revisions have become more frequent since 2011, which makes it more difficult to conduct within-benchmark analysis.

  3. Real-time PCE and Real-time core PCE were obtained from the Real-Time Data Research Center at the Federal Reserve Bank of Philadelphia (2018a, b)

  4. For more on the exclusion-from-core inflation-persistence model please see Johnson (1999), Clark (2001), Coogley (2002), Rich and Steindel (2005), Lafléche and Armour (2006), and Tierney (2011, 2012).

  5. For more information regarding the nonparametric methodology, see Ruppert and Wand (1994), Atkeson et al. (1997), Pagan and Ullah (1999), and Tierney (2011, 2012).

  6. The average residual squares criterion (ARSC) is used to approximate the IRSC for this paper.

  7. Other papers that use the residual-based window, include Cai (2007), Cai et al. (2006), Cai, Fan, and Yao (2000), Chauvet and Tierney (2009), Fan and Yao (1998), and Fujiwara and Koga (2004).

  8. The global nonparametric method is not the preferred method of using nonparametrics because the error terms are not obtained by minimizing the mean squared error.

  9. For more on the forecasting of the OLS exclusion-from-core inflation persistence model, please see Rich and Steindel (2005).

  10. Recall fz stands for the three nonparametric forecasting methods: f1, f2 and f3, and the length of the forecast horizon is g with g = {1, …, 12}.

  11. See Croushore and Stark (2001) and Croushore (2008) for more information regarding the data collection methods of the real-time dataset.

  12. The last newly released observation corresponding to 1995:Q4 for V_1996:Q1 is not given, so this value is interpolated from the last two values.

  13. A complete table of window widths is in Online Supplemental Appendix Table 1.

  14. Dean Croushore has graciously provided the following information: (i.) there are two different data sources for V_2000:Q1; (ii.) there is no data problem for vintages V_2007:Q2 to V_2008:Q2; and (iii.) he also provided the interpolation method used to obtain the last observation of V_1996:Q1.

  15. Online Supplemental Appendix Tables 2 and 3 list the vintages with large outliers regarding the RMSE and MAE.

  16. In order to test for structural breaks, the Bai-Quant Structural Break Test, the Quandt-Andrews Test, and the Andrews-Ploberger Test are applied to PCE and core PCE of V_2011:Q2 through the use of Bruce Hansen’s (2001) program for testing for structural changes.

  17. More information on the ability of the local nonparametric model to detect changes to the regression parameters due to data revisions can be found in Tierney (2011).

  18. Detailed tables of the parametric, global nonparametric and local nonparametric forecast standard deviations for all 62 vintages and for all 5 in-sample forecast horizons are available upon request. The forecast standard deviations for V_1999:Q4 and V_2000:Q1 are not presented due to a sparsity of data for V_1999:Q4 and the data for V_2000:Q1 being from two different sources.

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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.

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  1. Economics Department, Doermer School of Business, Purdue University Fort Wayne, 2101 Coliseum Boulevard East, Neff Hall 340B, Fort Wayne, IN, 46805, USA

    Heather L. R. Tierney

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Correspondence to Heather L. R. Tierney.

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Tierney, H.L.R. Forecasting with the Nonparametric Exclusion-from-Core Inflation Persistence Model Using Real-Time Data. Int Adv Econ Res 25, 39–63 (2019). https://doi.org/10.1007/s11294-019-09726-7

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