Patterns in IMF Growth Forecast Revisions: A Panel Study at Multiple Horizons

By Metodij Hadzi-Vaskov, Luca A Ricci, Alejandro Mariano Werner, Rene Zamarripa

May 7, 2021

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Metodij Hadzi-Vaskov, Luca A Ricci, Alejandro Mariano Werner, and Rene Zamarripa. Patterns in IMF Growth Forecast Revisions: A Panel Study at Multiple Horizons, (USA: International Monetary Fund, 2021) accessed November 7, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

This paper investigates the performance of the IMF WEO growth forecast revisions across different horizons and country groups. We find that: (i) growth revisions in horizons closer to the actual are generally larger, more volatile, and more negative; (ii) on average, growth revisions are in the right direction, becoming progressively more responsive to the forecast error gap as horizons get closer to the actual year; (iii) growth revisions in systemic economies are relevant for growth revisions in all country groups; (iv) WEO and Consensus Forecast growth revisions are highly correlated; (v) fall-to-spring WEO revisions are more correlated with Consensus Forecasts revisions compared to spring-to-fall revisions; and (vi) across vintages, revisions for a given time horizon are not autocorrelated; within vintages, revisions tend to be positively correlated, suggesting perception of persistent short-term shocks.

Subject: Econometric analysis, Economic forecasting, Economic sectors, Financial crises, International trade, Technology, Technology transfer, Terms of trade, Time series analysis, Vector autoregression

Keywords: Caribbean, Consensus forecast growth revision, Consensus forecasts revision, Europe, Fall-to-spring WEO revision, Growth revision, Technology transfer, Terms of trade, Time series analysis, Vector autoregression, WEO growth forecast

Publication Details

Pages:

57
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2021/136
Stock No:

WPIEA2021136
ISBN:

9781513573663
ISSN:

1018-5941

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