## 2003): “Forecast uncertainties in macroeconometric modelling: an application to the UK economy

Venue: | Journal of the American Statistical Association |

Citations: | 62 - 21 self |

### BibTeX

@ARTICLE{Garratt_2003):“forecast,

author = {Anthony Garratt and Kevin Lee and M. Hashem Pesaran},

title = {2003): “Forecast uncertainties in macroeconometric modelling: an application to the UK economy},

journal = {Journal of the American Statistical Association},

year = {},

pages = {829--838}

}

### Years of Citing Articles

### OpenURL

### Abstract

This paper argues that probability forecasts convey information on the uncertainties that surround macro-economic forecasts in a straightforward manner which is preferable to other alternatives, including the use of confidence intervals. Probability forecasts obtained using a small benchmark macroeconometric model as well as a number of other alternatives are presented and evaluated using recursive forecasts generated over the period 1999q1-2001q1. Out of sample probability forecasts of inflation and output growth are also provided over the period 2001q2-2003q1, and their implications discussed in relation to the Bank of England’s inflation target and the need to avoid recessions, both as separate events and jointly. The robustness of the results to parameter and model uncertainties is also investigated by a pragmatic implementation of the Bayesian model averaging approach.

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