Quantifying Uncertainties in Global Growth Forecasts

Ohnsorge, Franziska; Stocker, Marc; Some, Modeste

doi:10.1596/1813-9450-7770

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…A quantification of uncertainty around the global growth outlook suggests a wide range of possible outcomes, while confirming the predominance of downside risks (Ohnsorge, Stocker, and Some 2016). At current market conditions, the probability of global growth being more than 1 percentage point below baseline in 2019 is estimated at 21 percent, while that of growth being more than 1 percentage point above baseline is 16 percent.…”

Section: Per Capita Growth By Region D Share Of Emdes Catching Up To Advanced-economy Gdp Per Capita Levelsmentioning

confidence: 76%

“…Probabilities for 2019 are computed from the forecast distribution of 18month-ahead oil price futures, S&P 500 equity price futures, and term spread forecasts. Each of the risk factor's weight is derived from the model described in Ohnsorge, Stocker, and Some (2016) support skill acquisition and adaptability. This would assist the process of integration in regional and global value chains, as well as bolster firms' ability to absorb new technologies and compete internationally.…”

Section: Figure 12 Global Risks and Policy Challengesmentioning

confidence: 99%

“…The fan chart shows the forecast distribution of global growth using time-varying estimates of the standard deviation and skewness extracted from the forecast distribution of three underlying risk factors (oil price futures, the S&P 500 equity price futures, and term spread forecasts). Each of the risk factor's weight is derived from the model described in Ohnsorge, Stocker, and Some (2016)…”

Section: Figure 116 Risks: Tilted To the Downsidementioning

confidence: 99%

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Global Economic Prospects, June 2018: The Turning of the Tide?

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Regional projections and write-ups were produced in coordination with country teams, country directors, and the offices of the regional chief economists. Sources: Haver Analytics, World Bank. A.C.E.F. Shaded areas indicate forecasts. A. EMDEs = emerging market and developing economies. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights. Data for 2017 are estimates. B. Figure shows Purchasing Managers' Index (PMI) for manufacturing output and new export orders. Readings above 50 indicate expansion in economic activity; readings below 50 indicate contraction. Last observation is April 2018. C. Simple average of GDP growth. Orange lines indicate interquartile ranges of growth in each group. D. Global GDP levels in constant 2010 U.S. dollars, indexed to 100 at start of expansion periods. Cycle dates based on global recessions and slowdowns identified in Kose and Terrones (2015). Dashed line corresponds to 2018-20 forecasts. E. Trade measured as the average of export and import volumes. F. SAR = South Asia and SSA = Sub-Saharan Africa. GDP per capita calculated using constant 2010 U.S. dollar GDP weights. Click here to download data and charts. A. Global growth B. Global manufacturing output and export orders C. Growth in commodity-exporting EMDEs D. Global GDP during expansion periods E. Global trade and investment growth, volumes F. Per capita EMDE GDP growth, by region B. Share of countries with upgrades in 10-year-ahead growth forecasts D. Global growth forecasts, by forecast horizon A. Ten-year-ahead global growth forecasts C. One-to three-year-ahead global growth forecasts FIGURE 1.1.1 Growth forecasts: Global, groups, and aggregates After a prolonged period of downgrades, long-term forecasts of global growth, per capita growth, investment, and consumption may have stabilized in 2018, while short-term forecasts have been upgraded recently. This still leaves current long-term forecasts considerably lower than a decade ago. Downgrades were particularly steep, but started later (after the global financial crisis), for EMDEs than for advanced economies. Sources: Consensus Economics, United Nations, World Bank. Notes: Sample includes 38 countries, consisting of 20 advanced economies and 18 EMDEs, for which consensus forecasts are consistently available during 1998-2018. These economies account for 87 percent of global GDP over 2010-18. Unless otherwise noted, annual averages of results from multiple surveys conducted in each year are presented. A.B.E.F. The horizontal axis refers to the year of consensus forecast surveys. A.C.D.E. Global, advanced-economy, and EMDE growth is computed with constant 2010 U.S. dollar GDP weights. A. Per capita global output growth is computed as the difference between 10-year-ahead global growth forecasts and population growth estimates in the years for which forecast surveys are conducted. B. Share of countries with positive changes in 10-year-ahead growth forecasts from the previous year. C. Lines are based on consensus forecast surveys conducted in Septembe...

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Section: Per Capita Growth By Region D Share Of Emdes Catching Up To Advanced-economy Gdp Per Capita Levelsmentioning

confidence: 76%

Section: Figure 12 Global Risks and Policy Challengesmentioning

confidence: 99%

Section: Figure 116 Risks: Tilted To the Downsidementioning

confidence: 99%

See 1 more Smart Citation

Global Economic Prospects, June 2018: The Turning of the Tide?

2018

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“…The variance and skew of these distributions are then mapped to the confidence intervals around the projections for global GDP growth using weights obtained with OLS regressions, thereby imposing linearity. Ohnsorge et al (2016) proposed a similar approach based on vector autoregression models with time-varying weights that depend on the horizon of interest. In this (still linear) framework, the weights for the dispersion are obtained from the variance decomposition (i.e., share of global GDP growth forecast error variance explained by each risk factor) and the weights for the skewness are obtained from the impulse responses.…”

Section: Introductionmentioning

confidence: 99%

Predictive Density Aggregation

Caselli¹,

Grigoli²,

Lafarguette³

et al. 2020

IMF Working Papers

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In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries’ predictive GDP growth densities, taking into account cross-country interdependencies. Speci?cally, we model non-parametrically the contemporaneous interdependencies across the United States, the euro area, and China via a conditional kernel density estimation of a joint distribution. Then, we characterize the potential ampli?cation e?ects stemming from other large economies in each region—also with kernel density estimations—and the reaction of all other economies with para-metric assumptions. Importantly, each economy’s predictive density also depends on a set of observable country-speci?c factors. Finally, the use of sampling techniques allows us to aggregate individual countries’ densities into a world aggregate while preserving the non-i.i.d. nature of the global GDP growth distribution. Out-of-sample metrics con?rm the accuracy of our approach.

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Global Outlook: The Turning of the Tide?

2018

Global Economic Prospects

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Quantifying Uncertainties in Global Growth Forecasts

Cited by 3 publications

References 17 publications

Global Economic Prospects, June 2018: The Turning of the Tide?

Global Economic Prospects, June 2018: The Turning of the Tide?

Predictive Density Aggregation

Global Outlook: The Turning of the Tide?

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