Inference for Impulse Response Coefficients from Multivariate Fractionally Integrated Processes

quantf research Working Paper Series: WP13/2014

41 Pages Posted: 2 Jun 2014

See all articles by Richard Baillie

Richard Baillie

Michigan State University - The Eli Broad College of Business and The Eli Broad Graduate School of Management

George Kapetanios

King's College, London

Fotis Papailias

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

Date Written: June 1, 2014

Abstract

This paper considers a multivariate system of fractionally integrated time series and investigates the most appropriate way for estimating Impulse Response (IR) coefficients and their associated confidence intervals. The paper extends the univariate analysis recently provided by Baillie and Kapetanios (2013), and uses a semi parametric, time domain estimator, based on a vector autoregression (VAR) approximation. There are theoretical reasons for making the lag length of the VAR proportional to [ln(T)^2]. Results are also derived for the orthogonalized estimated IRs which are generally more practically relevant. Simulation evidence strongly indicates the desirability for applying the Kilian small sample bias correction, which is found to improve both the estimated orthogonalized and the non-orthogonalized IRs. The most appropriate order of the VAR turns out to be relevant for the lag length of the IR being estimated.

Keywords: ARFIMA Models, Impulse Response, Long Memory

Suggested Citation

Baillie, Richard and Kapetanios, George and Papailias, Fotis, Inference for Impulse Response Coefficients from Multivariate Fractionally Integrated Processes (June 1, 2014). quantf research Working Paper Series: WP13/2014. Available at SSRN: https://ssrn.com/abstract=2444419 or http://dx.doi.org/10.2139/ssrn.2444419

Richard Baillie

Michigan State University - The Eli Broad College of Business and The Eli Broad Graduate School of Management ( email )

East Lansing, MI 48824-1121
United States

George Kapetanios

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

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

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