Experience with Forecasting Univariate Time Series and the Combination of Forecasts

By P. Newbold, Clive W. J. Granger. 1974

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COMBINING FORECASTING PROCEDURES: SOME THEORETICAL RESULTS (2004, background): “…necessarily symmetric about 0+ When c~Yi 2 [yi ! is used as a loss function, asymmetry ofc is of interest to reflect the reality that over- and underforecasting might have very different consequences~cf+ Newbold and Granger, 1974!+ Becausec is convex, for any a0, there exists a liney 5 ua0~a 2 a0!”
Forecast combination and the Bank of England’s suite of statistical forecasting models (2008, unknown): “But the weight of evidence dating back to Bates and Granger (1969) and Newbold and Granger (1974) reveals that combinations of (9) This does not in itself imply that the underlying monetary stance changed. forecasts often outperform individual forecasts.”
Extrapolation for Time-Series and Cross-Sectional Data (2009, unknown): “…and Makridakis (1995) checked the SSCI and SCI (Science Citation Index) to determine the number of times researchers cited four major comparative validation studies on time series forecasting (Newbold and Granger 1974; Makridakis and Hibon 1979; Meese and Geweke 1984; and Makridakis et al. 1982).”
Forecasting and operational research: a review (2008, result): “Here, building on earlier work by Newbold and Granger (1974), Fildes (1979) offered some advice, while Makridakis and Hibon (1979) compared some 13 core extrapolative forecasting methods with the objective of reconciling the earlier evidence.”
To Combine Forecasts or to Combine Information? (2010, methodology): “15Starting from Granger and Ramanathan (1984), based on earlier works such as Bates and Granger (1969) and Newbold and Granger (1974), various feasible optimal combination weights have been suggested, which are static, dynamic, time-varying, or Bayesian: see Diebold and Lopez (1996).”
Combining expert forecasts: Can anything beat the simple average? (2013, background): “Such combinations have been shown to perform reasonably well in practice (Newbold and Granger (1974)), a finding which is often interpreted in terms of robustness given high estimation uncertainty that plagues other approaches attempting to exploit forecast co-variances.”