Tierney, Heather L.R. (2010): Real-Time Data Revisions and the PCE Measure of Inflation.
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Abstract
This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions.
Item Type: | MPRA Paper |
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Original Title: | Real-Time Data Revisions and the PCE Measure of Inflation |
Language: | English |
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 |
Item ID: | 22387 |
Depositing User: | Prof. Heather L.R. Tierney |
Date Deposited: | 30 Apr 2010 02:20 |
Last Modified: | 28 Sep 2019 14:03 |
References: | Atkeson CG, Moore AW, and Schaal S. Locally Weighted Learning. Artificial Intelligence Review 1997; 11; 11-73. Cai Z. Trending Time-Varying Coefficient Time Series Models with Serially Correlated Errors. Journal of Econometrics 2007; 136; 163–188. Cai Z, Chen R. Flexible Seasonal Time Series Models. In: Fomby BT, Terrell D (Eds), Advances in Econometrics: Honoring Engle and Granger, vol.20(2). Elsevier: Orlando; 2006. p. 63-87. Cai Z, Fan J, Yao Q. Functional-Coefficient Regression Models for Nonlinear Time Series. Journal of the American Statistical Association 2000; 95(451); 941-956. Chauvet M, Tierney HLR. Real-Time Changes in Monetary Transmission-A Nonparametric VAR Approach. Working Paper 2008. Choi CY. Reconsidering the Relationship between Inflation and Relative Price Variability. Working Paper 2009. Clark TE. Comparing Measures of Core Inflation. Federal Reserve Bank of Kansas City Economic Review 2001; 86(2); 5-31. Cleveland WS, Devlin JS. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting.” Journal of the American Statistical Association 1988; 83(403); 596-610. Cogley T. A Simple Adaptive Measure of Core Inflation. Journal of Money, Credit, and Banking 2002; 43(1); 94-113. Croushore D. Revisions to PCE Inflation Measures: Implications for Monetary Policy. Federal Reserve Bank of Philadelphia. Working Paper No. 08-8, 2008. Croushore D, Stark T. A Real-Time Data Set for Macroeconomists. Journal of Econometrics 2001; 105; 111–130. Croushore D, Stark T. A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter? The Review of Economics and Statistics 2003; 85(3); 605-617. Elliott G. Comments on 'Forecasting with a Real-Time Data Set for Macroeconomists'. Journal of Macroeconomics 2002; 24(4); 533-539. Fan J, Gijbels I. Data-Driven Selection in Polynomial Fitting: Variable Bandwidth and Spatial Adaptation. Journal of the Royal Statistical Society: Series B 1995; 57; 371-394. Fan J, Gijbels I. Monographs on Statistics and Applied Probability 66, Local Polynomial Modeling and Its Applications. Chapman and Hall: London; 1996. Fan J, Yao Q. Efficient Estimation of Conditional Variance Functions in Stochastic Regressions. Biometrika 1998; 85(3); 645-660. Fujiwara I, Koga M. 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 2004; 22(1); 123-142. Härdle W, Linton O. Applied Nonparametric Methods. In: Engle RF, Mc Fadden DL (Eds), Handbook of Econometrics, vol.IV. North-Holland: Amsterdam; 1994. Härdle W, Lütkepohl H, Chen R. A Review of Nonparametric Time Series Analysis. International Statistical Review/Revue Internationale de Statistique 1997; 65(1); 49-72. Härdle W, Tsybakov, A. Local Polynomial Estimator of the Volatility Function in Nonparametric Autoregression. Journal of Econometrics 1997; 81; 223-242. Howell DC. Fundamental Statistics for the Behavioral Sciences (6th International Edition). Thompson Learning: London; 2007. Johnson M. 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 1999. Kleinbaum DG, Kupper LL. Applied Regression Analysis and Other Multivariable Methods. Duxbury Press: Belmont; 1978. Lafléche T, Armour, J. Evaluating Measures of Core Inflation. Bank of Canada Review, Summer 2006. Lanne M. Nonlinear Dynamics of Interest Rate and Inflation. Journal of Applied Econometrics 2006; 21(8); 1157-1168. Marron JS. Automatic Smoothing Parameter Selection: A Survey. Empirical Economics 1988; 13; 187-208. Nobay B, Paya I, Peel DA. Inflation Dynamics in the US -A Nonlinear Perspective. Working Paper, 2007. Newey WK, West KD. A Simple, Positive, Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica 1987; 55(3); 765-775. Pagan A, Ullah A. Nonparametric Econometrics. Cambridge University Press: Cambridge; 1999. Rich R, Steindel C. A Review of Core Inflation and an Evaluation of Its Measures. Federal Reserve Bank of New York Staff Report No. 236, December 2005. Robinson PM. Inference Without-Smoothing in the Presence of Autocorrelation. Econometrica 1998; 66(5); 1163-1182. Ruppert D, Wand MP. Multivariate Locally Weighted Least Squares Regression. The Annals of Statistics 1994; 22; 1346-1370. Tierney HLR. A Nonparametric Study of Inflation Persistence with Real-Time Data: Using Exclusions-from-Core Measures of Inflation. VDM Verlag: Saarbrücken; 2009. Wand MP, Jones MC. Kernel Smoothing. Chapman & Hall: London; 1995. Wasserman L. All of Nonparametric Statistics. Springer: New York; 2006. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/22387 |