Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison
Henri Nyberg and
MPRA Paper from University Library of Munich, Germany
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 in terms of the mean square and mean absolute forecast errors. For a set of 18 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 (search for similar items in EconPapers)
JEL-codes: C22 C53 E37 E47 (search for similar items in EconPapers)
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