Does noncausality help in forecasting economic time series?

In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models. For a collection of quarterly time series, the improvement in forecast accuracy due to allowing for noncausality is found even greater.

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Year of publication:	2012
Authors:	Nyberg, Henri ; Lanne, Markku ; Saarinen, Erkka
Published in:	Economics Bulletin. - AccessEcon, ISSN 1545-2921. - Vol. 32.2012, 4, p. 2849-2859
Publisher:	AccessEcon
Subject:	Noncausal autoregression \| forecast comparison \| macroeconomic variables \| financial variables

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Extent:	application/pdf
Type of publication:	Article
Classification:	C5 - Econometric Modeling ; C2 - Econometric Methods: Single Equation Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011278622