Long-Memory Forecasting of U.S. Monetary Indices
AbstractSeveral studies have tested for long-range dependence in macroeconomic and financial time series but very few have assessed the usefulness of long-memory models as forecast generating mechanisms. This study tests for fractional differencing in the U.S. monetary indices (simple sum and divisia) and compares the out-of-sample fractional forecasts to benchmark forecasts. The long-memory parameter is estimated using RobinsonÃ•s Gaussian semiparametric and multivariate log-periodogram methods. The evidence amply suggests that the monetary series possess a fractional order between one and two. Fractional out-of-sample forecasts are consistently more accurate (with the exception of the M3 series) than benchmark autoregressive forecasts but the forecasting gains are not generally statistically significant. In terms of forecast encompassing, the fractional model encompasses the autoregressive model for the divisia series but neither model encompasses the other for the simple sum series.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 558.
Length: 23 pages
Date of creation: 01 May 2003
Date of revision:
Publication status: published, Journal of Forecasting, 2006, 25, 291-302
Contact details of provider:
Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA
Web page: http://fmwww.bc.edu/EC/
More information through EDIRC
long memory; ARFIMA model; macroeconomic forecasting.;
Other versions of this item:
- E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-05-08 (All new papers)
- NEP-ECM-2003-05-15 (Econometrics)
- NEP-ETS-2003-05-08 (Econometric Time Series)
- NEP-MAC-2003-05-08 (Macroeconomics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Franses, Philip Hans & Ooms, Marius, 1997. "A periodic long-memory model for quarterly UK inflation," International Journal of Forecasting, Elsevier, vol. 13(1), pages 117-126, March.
- Diebold, Francis X & Rudebusch, Glenn D, 1991.
"Is Consumption Too Smooth? Long Memory and the Deaton Paradox,"
The Review of Economics and Statistics,
MIT Press, vol. 73(1), pages 1-9, February.
- Francis X. Diebold & Glenn D. Rudebusch, 1989. "Is consumption too smooth? Long memory and the Deaton paradox," Finance and Economics Discussion Series 57, Board of Governors of the Federal Reserve System (U.S.).
- Sowell, Fallaw, 1992. "Modeling long-run behavior with the fractional ARIMA model," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 277-302, April.
- Diebold, Francis X & Mariano, Roberto S, 1995.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 253-63, July.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Cheung, Yin-Wong, 1993. "Long Memory in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 93-101, January.
- Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
- Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
- John Barkoulas & Christopher F. Baum, 1996.
"Fractional Differencing Modeling and Forecasting of Eurocurrency Deposit Rates,"
Boston College Working Papers in Economics
317., Boston College Department of Economics.
- Barkoulas, John T & Baum, Christopher F, 1997. "Fractional Differencing Modeling and Forecasting of Eurocurrency Deposit Rates," Journal of Financial Research, Southern Finance Association & Southwestern Finance Association, vol. 20(3), pages 355-72, Fall.
- Christopher F. Baum & John T. Barkoulas & Mustafa Caglayan, 1999.
"Persistence in International Inflation Rates,"
Southern Economic Journal,
Southern Economic Association, vol. 65(4), pages 900-913, April.
- Diebold, Francis X. & Lindner, Peter, 1996. "Fractional integration and interval prediction," Economics Letters, Elsevier, vol. 50(3), pages 305-313, March.
- Clements,Michael & Hendry,David, 1998.
"Forecasting Economic Time Series,"
Cambridge University Press, number 9780521634809, April.
- Diebold, Francis X. & Rudebusch, Glenn D., 1989.
"Long memory and persistence in aggregate output,"
Journal of Monetary Economics,
Elsevier, vol. 24(2), pages 189-209, September.
- John Barkoulas & Christopher F. Baum & Mustafa Caglayan, 1998.
"Fractional Monetary Dynamics,"
Boston College Working Papers in Economics
321., Boston College Department of Economics.
- Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
- Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
- Guglielmo Maria Caporale & Luis A. Gil-Alana, 2009.
"Multi-Factor Gegenbauer Processes and European Inflation Rates,"
Discussion Papers of DIW Berlin
879, DIW Berlin, German Institute for Economic Research.
- Guglielmo Maria Caporale & Luis A. Gil-Alana, 2009. "Multi-Factor Gegenbauer Processes and European Inflation Rates," CESifo Working Paper Series 2648, CESifo Group Munich.
- S. D. Grose & D. S. Poskitt, 2006. "The Finite-Sample Properties of Autoregressive Approximations of Fractionally-Integrated and Non-Invertible Processes," Monash Econometrics and Business Statistics Working Papers 15/06, Monash University, Department of Econometrics and Business Statistics.
- Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2013.
"SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence,"
Society for Computational Economics, vol. 41(2), pages 249-265, February.
- Mohamed Chikhi & Anne Peguin-Feissolle & Michel Terraza, 2012. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," Working Papers halshs-00793203, HAL.
- Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2012. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," AMSE Working Papers 1214, Aix-Marseille School of Economics, Marseille, France.
- Fernandez, Viviana, 2010. "Commodity futures and market efficiency: A fractional integrated approach," Resources Policy, Elsevier, vol. 35(4), pages 276-282, December.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F Baum).
If references are entirely missing, you can add them using this form.