Tierney, Heather L.R. (2009): Examining the Ability of Core Inflation to Capture the Overall Trend of Total Inflation.
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This paper examines whether core inflation is able to predict the overall trend of total inflation using real-time data in a parametric and nonparametric framework. Specifically, two sample periods and five in-sample forecast horizons in two measures of inflation, which are the personal consumption expenditure and the consumer price index, are used in the exclusions-from core inflation persistence model. This paper finds that core inflation is only able to capture the overall trend of total inflation for the twelve-quarter in-sample forecast horizon using the consumer price index in both the parametric and nonparametric models in the longer sample period. The nonparametric model outperforms the parametric model for both data samples and for all five in-sample forecast horizons.
|Item Type:||MPRA Paper|
|Original Title:||Examining the Ability of Core Inflation to Capture the Overall Trend of Total Inflation|
|Keywords:||Inflation Persistence, Real-Time Data, Monetary Policy, Nonparametrics, In-Sample 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:||30. Apr 2010 02:09|
|Last Modified:||12. Feb 2013 14:41|
Atkeson, C.G., Moore, A.W., and Schaal, S. (1997), “Locally Weighted Learning,” Artificial Intelligence Review, 11, 11-73.
Bai, J. (1997), “Estimating Multiple Breaks One at a Time,” Economic Theory, 13:3, 315-352.
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. (2005), “Flexible seasonal time series models,” Advances in Econometrics Volume Honoring Engle and Granger, B. T. Fomby and D. Terrell, eds., Orlando: Elsevier.
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.
Cai, Z., Kuan, C., and Sun, L. (2008), “Nonparametric Pricing Kernel: Estimation and Test,” Working Paper.
Chauvet, M. and Tierney, H.L.R. (2008), “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.
Creel, M. (2008), “Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments,” Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC), UFAE and IAE Working Papers 725.08, revised June.
Croushore, D. (2007), “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.
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.
Fan, J., Zhang, C., and Zhang, J. (2001), “Generalized Likelihood Ratio Statistics and Wilks Phenomenon” The Annals of Statistics, 29, 153-193.
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.
Gagnon, J.E. (2008), “Inflation Regimes and Inflation Expectations,” Federal Reserve Bank of St. Louis Review, 90:3-Part 2.
Hansen, B.E. (2001), GAUSS Program for Testing for Structural Change, http://www.ssc.wisc.edu/~bhansen/progs/jep_01.htm. Accessed 5 Dec 2008.
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 Mammen, E. (1993), “Comparing Nonparametric versus Parametric Regression Fits,” The Annals of Statistics, 21:4, 1926-1947.
Härdle, W. and Tsybakov, A. (1997), “Local Polynomial Estimator of the Volatility Function in Nonparametric Autoregression,” Journal of Econometrics, 81, 223-242.
Hayfield, T. and Racine, J. (2008), “Nonparametric Econometrics: The NP Package,” Journal of Statistical Software, 27:5, 1-32.
Höppner, F., Melzer, C., and Neumann, T. (2008), “Changing Effects of Monetary Policy in the US-evidence from a Time-varying Coefficient VAR,” Applied Economics, 40:18, 2353-2360.
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.
Newey, W.K., and West, K.D. (1987), “A Simple, Positive, Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica, 55:3, 765-775.
Paez-Farrell, J. (2009), "Monetary policy rules in theory and in practice: evidence from the UK and the US," Applied Economics, 41:16, 2037-2046.
Pagan, A and Ullah, A. (1999), Nonparametric Econometrics, Cambridge: Cambridge University Press.
Rapach, D. (2003), “International Evidence on the Long-Run Impact of Inflation,” Journal of Money Credit and Banking, 35:1, 23-45.
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.
Robinson, P.M. (1998), “Inference Without-Smoothing in the Presence of Autocorrelation,” Econometrica, 66:5, 1163-1182.
Ruppert, D. and Wand, M. P. (1994), “Multivariate Locally Weighted Least Squares Regression,” The Annals of Statistics, 22, 1346-1370.
Silver, M. (1997), “Core Inflation Measure and Statistical Issues in Choosing among Them,” International Monetary Fund Working Paper, WP/06/97.
Vaihekoski, M. (2004), “nwest”, GAUSS Procedure for the Newey-West heteroskedasticity and autocorrelation consistent (HAC) covariance matrix, http://www2.lut.fi/~vaihekos/mv_econ.html#e3. Accessed 8 July 2008.
Wand, M. P. and Jones, M.C. (1995), Kernel Smoothing, Chapman & Hall, London.
Wasserman, L. (2006), All of Nonparametric Statistics, Springer, New York.