Forecasting Performance of Information Criteria with Many Macro Series

Clive Granger and Yongil Jeon

Journal of Applied Statistics, 2004, vol. 31, issue 10, 1227-1240

Abstract: Stock & Watson (1999) consider the relative quality of different univariate forecasting techniques. This paper extends their study on forecasting practice, comparing the forecasting performance of two popular model selection procedures, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). This paper considers several topics: how AIC and BIC choose lags in autoregressive models on actual series, how models so selected forecast relative to an AR(4) model, the effect of using a maximum lag on model selection, and the forecasting performance of combining AR(4), AIC, and BIC models with an equal weight.

Keywords: Large macro model; information criterion; AIC; BIC (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (22)

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DOI: 10.1080/0266476042000285495

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