Forecast Uncertainties in Macroeconomic Modeling

By Anthony Garratt, Kevin Lee, M. Pesaran, Yongcheol Shin. 2003

101 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c3b08c69ce95f2f2c5e7a24035266d4e2d8f46b3

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In Economics and Econometrics

Citations in the corpus

Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR 'Fan' Charts of Inflation (2005, unknown): “5 Garratt et al. (2003) and Pesaran & Zaffaroni (2004) have also considered the combination of probability forecasts using BMA.”
Path Forecast Evaluation (2010, unknown): “Similarly, the assumption of Gaussian errors could be relaxed, but then the distribution of the forecast errors would no longer be Normal and should be obtained by means of simulation methods, see e.g. Garratt et al. (2003).”
Probabilistic forecasts, calibration and sharpness (2007, methodology): “Formal tests of uniformity can be employed and have been studied by Anderson (1996), Talagrand et al. (1997), Noceti et al. (2003), Garratt et al. (2003), Wallis (2003), Candille and Talagrand (2005) and Corradi and Swanson (2006), among others.”
Macroeconometric Modelling with a Global Perspective (2006, methodology): “This would be consistent with the view advanced in Garratt et al. (2003a) that theory is likely to provide more information about long-run relations than about the short-run dynamics.”
Least-squares forecast averaging (2008, background): “Some applications in econometrics include Sala-i-Martin et al. (2004), Brock and Durlauf (2001), Avramov (2002), Fernandez et al. (2001a,b), Garratt et al. (2003) and Brock et al. (2003).”
Testing Forecast Optimality Under Unknown Loss (2007, unknown): “One possible extension of our work includes establishing results for density forecasting, an area recently considered by Garratt et al. (2003) and Campbell and Diebold (2005), although applications of such results to empirical data will be limited, because currently the vast majority of published…”
Forecast combinations: An over 50-year review (2022, background): “BMA offers a conceptually elegant and logically coherent solution to the issue of accounting for model uncertainty (see, e.g., Leamer, 1978; Draper, 1995; Raftery et al., 1997; Garratt et al., 2003).”
Likelihood-based scoring rules for comparing density forecasts in tails (2011, background): “Undoubtedly, this is due to the increased awareness that point forecasts are not very informative unless some indication of their uncertainty is provided; see Granger and Pesaran (2000) and Garratt et al. (2003) for discussions of this issue.”