Forecasts in times of crises

Eicher, Theo S.; Kuenzel, David J.; Papageorgiou, Chris; Christofides, Charis

doi:10.1016/j.ijforecast.2019.04.001

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…Our paper also relates to the literature on sources of bias and inefficiency in macroeconomic forecasts made by the IMF and by other forecasters (Timmermann 2007 ; Eicher et al 2019 ). Blanchard and Leigh ( 2013 ) provide a prominent example of inefficient program forecasts: the underestimation of the impact of a planned fiscal consolidation on growth during the Euro area crisis of 2010–2011.…”

Section: Introductionmentioning

confidence: 76%

Macrofinancial Causes of Optimism in Growth Forecasts

Carrière‐Swallow¹,

Marzluf²

2022

IMF Econ Rev

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We analyze the causes of the apparent bias toward optimism in growth forecasts underpinning the design of IMF-supported programs, which has been documented in the literature. We find that financial variables observable to forecasters are strong predictors of growth forecast errors. The greater the expansion of the credit-to-GDP gap in the years preceding a forecast, the greater its over-optimism about growth over the next two years. This result is strongest among forecasts that were most optimistic, where errors are also increasing in the economy’s degree of liability dollarization. We find that the inefficient use of financial information applies to growth forecasts more broadly, including the IMF’s forecasts in the World Economic Outlook and those produced by professional forecasters compiled by Consensus Economics. We conclude that improved macrofinancial analysis represents a promising avenue for reducing over-optimism in growth forecasts.

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Section: Introductionmentioning

confidence: 76%

Macrofinancial Causes of Optimism in Growth Forecasts

Carrière‐Swallow¹,

Marzluf²

2022

IMF Econ Rev

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“…As shown by Eicher et al (2019), during crises forecasts are likely to exhibit large forecast errors, and such forecast errors are often unpredictable; therefore, the two crises are taken into account by the use of dummy variables. Furthermore, the use of dummy variables is motivated by the fact that the GDP growth forecasts by professional forecasters for advanced economies are subject to large negative systematic errors, that is, over‐prediction, during recessions (Dovern and Jannsen 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Many studies investigated the various forecasts produced by the IMF (Artis 1988, 1996; Barrionuevo 1993; Genberg and Martinez 2014; An et al 2019), the OECD (Smyth and Ash 1975; Smyth 1983; Holden et al 1987; Ash et al 1990, 1997, 1998; Vogel 2007), and the EC (Keereman 1999; Melander et al 2007). Eicher et al (2019) evaluated IMF's forecasts for countries during crises in terms of bias and efficiency, and found that these forecasts are inefficient and exhibit significant biases, particularly for low‐income countries.…”

Section: Related Literaturementioning

confidence: 99%

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Thirty‐year assessment of Asian Development Bank's forecasts

Tsuchiya

2021

Asian-Pac Economic Lit

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This study assesses the performance of the forecasts of the Asian Development Bank (ADB) for 45 countries between 1989 and 2019 with the following results: first, forecast accuracy and bias vary among countries and across variables; second, the Asian currency and global financial crises adversely affected the GDP and inflation forecast accuracy, whereas there were no such impacts on the current account forecast accuracy; third, the ADB's forecasts are broadly unbiased; fourth, inflation forecasts are efficient but the GDP and current account forecasts are not; finally, unbiasedness is broadly robust for the countries' regions. These results suggest that the ADB plays a grounding economic role in providing a reliable economic outlook for policymakers and businesses world wide.

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“…Papaioannou et al (2013) find that elevated macroeconomic volatility and market-risk uncertainty can make it difficult for investors to distinguish temporary price fluctuations from more fundamental changes in risk. Macroeconomic monitoring informs risk assessments by measuring expected losses, and Eicher et al (2019) find that the poor quality of high-frequency data in many low-income countries can diminish the ability to grasp the timing of downturn and upturn cycles. In addition, frequent statistical revisions negatively affect the ability of analysts to separate cyclical fluctuations from secular macroeconomic trends, complicating efforts to precisely date expansions and recessions.…”

Section: (Ii) Real-time Macroeconomic Uncertaintymentioning

confidence: 99%