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Henri Nyberg, Markku Lanne and Erkka Saarinen
 
''Does noncausality help in forecasting economic time series?''
( 2012, Vol. 32 No.4 )
 
 
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.
 
 
Keywords: Noncausal autoregression, forecast comparison, macroeconomic variables, financial variables
JEL: C5 - Econometric Modeling: General
C2 - Single Equation Models; Single Variables: General
 
Manuscript Received : May 10 2012 Manuscript Accepted : Oct 11 2012

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