Forecasting and Tracking Real-Time Data Revisions in Inflation Persistence

Tierney, Heather L.R. (2013): Forecasting and Tracking Real-Time Data Revisions in Inflation Persistence.

Abstract

The purpose of this paper is to examine the forecasting ability of sixty-two vintages of revised real-time PCE and core PCE using nonparametric methodologies. The combined fields of real-time data and nonparametric forecasting have not been previously explored with rigor, which this paper remedies. The contributions of this paper are on the three fronts of (i.) analysis of real-time data; (ii.) the additional benefits of using nonparametric econometrics to examine real-time data; and (iii.) nonparametric forecasting with real-time data. Regarding the analysis of real-time data revisions, this paper finds that the third quarter releases of real-time data have the largest number of data revisions. Secondly, nonparametric regressions are beneficial in utilizing the information provided by data revisions, which typically are just a few tenths in magnitude but are significant enough to statistically affect regression results. The deviations in window widths can be useful in identifying potential problematic time periods such as a large spike in oil prices. The third and final front of this paper regards nonparametric forecasting and the best performing real-time data release with the three local nonparametric forecasting methods outperforming the parametric benchmark forecasts. Lastly, this paper shows that the best performing quarterly-release of real-time data is dependent on the benchmark revision periods. For vintages 1996:Q1 to 2003:Q3, the second quarter real-time data releases produce the smaller RMSE 58% of the time and for vintages 2003:Q4 to 2011:Q2, the third quarter real-time data releases produce forecasts with smaller RMSE approximately 60% of the time.

• Item Type: MPRA Paper
• Language: English
• Keywords: Nonparametric Forecasting, Real-Time Data, Monetary Policy, Inflation Persistence
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy

References

Atkeson CG, Moore AW, Schaal S. 1997. Locally Weighted Learning. Artificial Intelligence Review. 11: 11-73.

Barkoulas JT, Baum CF, Onochie J. 1997. Nonparametric Investigation of the 90-Day T-Bill Rate. Review of Financial Economics. 6: 187-198.

Cai Z. 2007. Trending Time-Varying Coefficient Time Series Models with Serially Correlated Errors. Journal of Econometrics. 136: 63–188.

Cai Z, Chen R. 2006. Flexible Seasonal Time Series Models. Advances in Econometrics Volume Honoring Engle and Granger. B. T. Fomby and D. Terrell. eds. Orlando: Elsevier: 63-87.

Cai Z, Fan J, Yao Q. 2000. Functional-Coefficient Regression Models for Nonlinear Time Series. Journal of the American Statistical Association. 95: 941-956.

Chauvet M, Tierney HLR. 2009. Real-Time Changes in Monetary Transmission—A Nonparametric VAR Approach. Working Paper.

Clark TE. 2001. Comparing Measures of Core Inflation. Federal Reserve Bank of Kansas City Economic Review. 86: 5-31.

Cleveland WS, Devlin SJ. 1988. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting. Journal of the American Statistical Association. 83: 596-610.

Cogley T. 2002. A Simple Adaptive Measure of Core Inflation. Journal of Money. Credit. and Banking. 43: 94-113.

Croushore D. 2008. Revisions to PCE Inflation Measures: Implications for Monetary Policy. Federal Reserve Bank of Philadelphia. Working Paper.

Croushore D, Stark T. 2001. A Real-Time Data Set for Macroeconomists. Journal of Econometrics 105: 111-130.

Croushore D, Stark. T. 2003. A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter? The Review of Economics and Statistics. 8: 605-617.

Davidson AC, Hinkley DV. 2006. Bootstrap Methods and their Applications. Cambridge: New York.

De Brabanter K, De Brabanter J, Gijbels I, De Moor B. 2013. Derivative Estimation with Local Polynomial Fitting. Journal of Machine Learning Research. 14: 281-301.

De Brabanter K, De Brabanter J, Suykens JAK, De Moor B. 2011. Kernel Regression in the Presence of Correlated Errors. Journal of Machine Learning Research. 12. 1955 - 1976.

Diebold FX, Mariano RS. 1995. Comparing Predictive Accuracy. Journal of Business & Economic Statistics. 13: 253-63.

Diebold FX, Nason JA. 1990. Nonparametric Exchange Rate Prediction. Journal of International Economics. 28: 315-332.

Elliott G. 2002. Comments on 'Forecasting with a Real-Time Data Set for Macroeconomists'. Journal of Macroeconomics. 24: 533-539.

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: 371-394.

Fan J, Gijbels I. 1996. Monographs on Statistics and Applied Probability 66. Local Polynomial Modeling and Its Applications. London: Chapman and Hall.

Fan J, Yao Q. 1998. Efficient Estimation of Conditional Variance Functions in Stochastic Regressions. Biometrika. 85: 645-660.

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. 22:123-142.

Gooijer JGD, Gannoun A. 1999. Nonparametric Conditional Predictive Regions for Time Series. Computational Statistics & Data Analysis. 33: 259-275.

Hansen BE. 2001. GAUSS program for testing for structural change. Available at http://www.ssc.wisc.edu/_bhansen/progs/jep_01.htm. (Accessed 18 October 2011).

Gooijer JGD, Zerom D. 2000. Kernel-based Multistep-ahead Predictions of the US Short-term Interest Rate. Journal of Forecasting. 19: 335-353.

Härdle W. 1994. Applied Nonparametric Regression. Cambridge: Cambridge University Press.

Härdle W, Linton. O. 1994. Applied Nonparametric Methods. Handbook of Econometrics. IV. R.F. Engle and D.L. Mc Fadden. eds.. Amsterdam: North-Holland.

Härdle W, Lütkepohl H, Chen. R. 1997. A Review of Nonparametric Time Series Analysis. International Statistical Review / Revue Internationale de Statistique. 65: 49-72.

Härdle W, Tsybakov A. 1997. Local Polynomial Estimator of the Volatility Function in Nonparametric Autoregression. Journal of Econometrics. 81: 223-242.

Harvey DI, Leybourne SJ, Newbold P. 1997. Testing the Equality of Prediction Mean Squared Errors. International Journal of Forecasting.13: 281-291.

Harvey DI, Leybourne SJ, Newbold P. 1998. Tests for Forecast Encompassing. Journal of Business & Economic Statistics. 16:2. 254-259.

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.

Lafléche T, Armour J. 2006. Evaluating Measures of Core Inflation. Bank of Canada Review. Summer.

Li Q, Racine J. 2007. Nonparametrics Econometrics: Theory and Practice. Princeton University Press. Princeton.

Marron JS. 1988. Automatic Smoothing Parameter Selection: A Survey. Empirical Economics. 13: 187-208.

Matzner-Løfber E, Gannoun A, Gooijer JGD. 1998. Nonparametric Forecasting: A Comparison of Three Kernel-Based Methods. Communications in Statistics - Theory and Methods. 27:1532-1617.

Newey WK, West KD. 1987. A Simple. Positive. Definite. Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica. 55:3. 765-775.

Opsomer J, Wang Y, Yang. Y. 2001. Nonparametric Regression with Correlated Errors. Statistical Science. 16:134–153.

Pagan A, Ullah A. 1999. Nonparametric Econometrics. Cambridge: Cambridge University Press.

Pindyck RS, Rubinfeld DL 1998. Econometric Models and Economic Forecasts. 4th Edition. New York: McGraw-Hill.

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. December.

Ruppert D, Wand MP. 1994. Multivariate Locally Weighted Least Squares Regression. The Annals of Statistics. 22:1346-1370.

Tierney HLR. 2011. Real-Time Data Revisions and the PCE Measure of Inflation Economic Modelling. 28: 1763-1773.

Tierney HLR. 2012. Examining the Ability of Core Inflation to Capture the Overall Trend of Total Inflation. Applied Economics. 44: 493-514.

Vilar-Fernández JM, Cao R. 2007. Nonparametric Forecasting in Time Series: A Comparative Study. Communications in Statistics-Simulation and Computation. 36: 311-334.

Wand MP, Jones MC. 1995. Kernel Smoothing. Chapman & Hall. London.

Wasserman L. 2006. All of Nonparametric Statistics. Springer. New York.

White H. 1980. A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica 48: 817-838.