Long-Memory Forecasting of U.S. Monetary Indices

John Barkoulas and Christopher Baum

No 558, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: Several 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 RobinsonÕs 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.

Keywords: long memory; ARFIMA model; macroeconomic forecasting. (search for similar items in EconPapers)
JEL-codes: C22 C52 E51 (search for similar items in EconPapers)
Date: 2003-05-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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Citations: View citations in EconPapers (1)

Published, Journal of Forecasting, 2006, 25, 291-302

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Journal Article: Long-memory forecasting of US monetary indices (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocoec:558

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