Tierney, Heather L.R. (2011): Forecasting and tracking real-time data revisions in inflation persistence.
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This paper presents three local nonparametric forecasting methods that are able to utilize the isolated periods of revised real-time PCE and core PCE for 62 vintages within a historic framework with respect to the nonparametric exclusion-from-core inflation persistence model. The flexibility, provided by the kernel and window width, permits the incorporation of the forecasted value into the appropriate time frame. For instance, a low inflation measure can be included in other low inflation time periods in order to form more optimal forecasts by combining values that are similar in terms of metric distance as opposed to chronological time. The most efficient nonparametric forecasting method is the third model, which uses the flexibility of nonparametrics to its utmost by making forecasts conditional on the forecasted value.
|Item Type:||MPRA Paper|
|Original Title:||Forecasting and tracking real-time data revisions in inflation persistence|
|Keywords:||Inflation Persistence, Real-Time Data, Monetary Policy, Nonparametrics, Forecasting|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy
|Depositing User:||Heather L.R. Tierney|
|Date Deposited:||01. Nov 2011 23:28|
|Last Modified:||16. Feb 2013 09:27|
Atkeson, C.G., Moore, A.W., and Schaal, S. (1997), “Locally Weighted Learning,” Artificial Intelligence Review, 11, 11-73.
Barkoulas, J.T., Baum, C.F., and Onochie, J. (1997), “Nonparametric Investigation of the 90-Day T-Bill Rate,” Review of Financial Economics, 6:2, 187-198.
Cai, Z. (2007), “Trending Time-Varying Coefficient Time Series Models with Serially Correlated Errors,” Journal of Econometrics, 136, 163–188.
Cai, Z. and 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: Orlando, p. 63-87.
Cai, Z., Fan, J., and Yao, Q. (2000), “Functional-Coefficient Regression Models for Nonlinear Time Series,” Journal of the American Statistical Association, 95:451, 941-956.
Chauvet, M. and Tierney, H.L.R. (2009), “Real-Time Changes in Monetary Transmission —A Nonparametric VAR Approach,” Working Paper.
Clark, T.E. (2001), “Comparing Measures of Core Inflation,” Federal Reserve Bank of Kansas City Economic Review, 86:2 (Second Quarter), 5-31.
Cleveland, W.S. and Devlin, S.J. (1988), “Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting,” Journal of the American Statistical Association, 83:403, 596-610.
Cogley, T. (2002), “A Simple Adaptive Measure of Core Inflation,” Journal of Money, Credit, and Banking, 43:1, 94-113.
Croushore, D. (2008), “Revisions to PCE Inflation Measures: Implications for Monetary Policy,” Federal Reserve Bank of Philadelphia, Working Paper.
Croushore, D., and Stark, T. (2001), “A Real-Time Data Set for Macroeconomists,” Journal of Econometrics 105, 111-130.
Croushore, D., and 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.
Diebold, F.X. & Mariano, R.S. (1995), "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, 13:3, 253-63.
Diebold, F.X. & Nason, J.A., 1(990), "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, 24:4, 533-539.
Fan, J. and 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. and Gijbels, I. (1996), Monographs on Statistics and Applied Probability 66, Local Polynomial Modeling and Its Applications. London: Chapman and Hall.
Fan, J. and Yao, Q. (1998), “Efficient Estimation of Conditional Variance Functions in Stochastic Regressions,” Biometrika, 85:3, 645-660.
Fujiwara, I. and 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, March.
Gooijer, J.G.D. and Gannoun, A. (1999), "Nonparametric Conditional Predictive Regions for Time Series," Computational Statistics & Data Analysis, 33:3, 259-275.
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 October 2011).
Gooijer, J.G.D. and Zerom, D. (2000), "Kernel-based Multistep-ahead Predictions of the US Short-term Interest Rate," Journal of Forecasting, 19:4, 335-353.
Härdle, W. (1994), Applied Nonparametric Regression, Cambridge: Cambridge University Press.
Härdle, W. and Linton, O. (1994), “Applied Nonparametric Methods,” Handbook of Econometrics, IV, R.F. Engle and D.L. Mc Fadden, eds., Amsterdam: North-Holland.
Härdle, W. and Tsybakov, A. (1997), “Local Polynomial Estimator of the Volatility Function in Nonparametric Autoregression,” Journal of Econometrics, 81, 223-242.
Harvey,D.I., Leybourne, S.J., and Newbold, P. (1997), "Testing the Equality of Prediction Mean Squared Errors," International Journal of Forecasting, 13:2, 281-291.
Harvey,D.I., Leybourne, S.J., and Newbold, P. (1998), “Tests for Forecast Encompassing,” Journal of Business & Economic Statistics, 16:2, 254-259.
Johnson, Marianne (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. and Armour, J. (2006), “Evaluating Measures of Core Inflation,” Bank of Canada Review, Summer.
Li, Q. and Racine, J. (2007), Nonparametrics Econometrics: Theory and Practice, Princeton University Press, Princeton.
Marron, J.S. (1988), “Automatic Smoothing Parameter Selection: A Survey,” Empirical Economics, 13, 187-208.
Matzner-Løfber, E., Gannoun, A., and Gooijer, J.G.D. (1998), “Nonparametric Forecasting: A Comparison of Three Kernel-Based Methods,” Communications in Statistics - Theory and Methods, 27:7, 1532-1617.
Newey, W.K., and West, K.D. (1987), “A Simple, Positive, Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica, 55:3, 765-775.
Pagan, A and Ullah, A. (1999), Nonparametric Econometrics, Cambridge: Cambridge University Press.
Rich, R. and 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. and Wand, M.P. (1994), “Multivariate Locally Weighted Least Squares Regression,” The Annals of Statistics, 22, 1346-1370.
Tierney, H.L.R. (2011), “Real-Time Data Revisions and the PCE Measure of Inflation” Economic Modelling, 28:4, 1763-1773.
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 (Forthcoming).
Vilar-Fernández, J.M. and Cao, R. (2007), “Nonparametric Forecasting in Time Series: A Comparative Study,” Communications in Statistics-Simulation and Computation, 36:2, 311-334.
Wand, M.P. and Jones, M.C. (1995), Kernel Smoothing, Chapman & Hall, London.
Wasserman, L. (2006), All of Nonparametric Statistics, Springer, New York.