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.
Volume (Year): 32 (2012)
Issue (Month): 4 ()
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- Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006.
"A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series,"
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- Lanne, Markku & Nyberg, Henri & Saarinen, Erkka, 2011. "Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison," MPRA Paper 30254, University Library of Munich, Germany. Full references (including those not matched with items on IDEAS)