Forecasting Strongly Dependent Macroeconomic and Monetary Series: A Two-Stage Approach and a Direct High-Order Autoregression

quantf research Working Paper Series: WP12/2014

50 Pages Posted: 2 Jun 2014  

Fotis Papailias

Quantf Research; University of London, King's College London, Department of Management

Gustavo Fruet Dias

Aarhus University; University of Aarhus - CREATES

Date Written: June 1, 2014

Abstract

A two step forecasting approach for long memory time series is introduced. In the first step we estimate the fractional exponent and, applying the fractional differencing operator, we obtain the underlying weakly dependent series. In the second step, we perform the multi-step ahead forecasts for the weakly dependent series and obtain their long memory counterparts by applying the fractional cumulation operator. The methodology applies to stationary and nonstationary cases. Applications to sixteen macroeconomic and monetary series indicate that the new methodology provides better forecasts. Furthermore, a high-order AR model fitted to the original data also yields to comparable results.

Keywords: Forecasting, Infinite Autoregressions, Long Memory, MLE, Local Whittle

Suggested Citation

Papailias, Fotis and Fruet Dias, Gustavo, Forecasting Strongly Dependent Macroeconomic and Monetary Series: A Two-Stage Approach and a Direct High-Order Autoregression (June 1, 2014). quantf research Working Paper Series: WP12/2014. Available at SSRN: https://ssrn.com/abstract=2444417 or http://dx.doi.org/10.2139/ssrn.2444417

Fotis Papailias (Contact Author)

Quantf Research ( email )

London
United Kingdom

HOME PAGE: http://www.quantf.com

University of London, King's College London, Department of Management ( email )

150 Stamford Street
London, SE1 9NN
United Kingdom

Gustavo Fruet Dias

Aarhus University ( email )

Nordre Ringgade 1
Aarhus, 8000
Denmark

University of Aarhus - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

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