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.
|Number of pages||11|
|Publication status||Published - Oct 2012|
|MoE publication type||A1 Journal article-refereed|
Fields of Science
- 511 Economics