By Philip Hans Franses and Max Welz

“This paper deals with forecasting low-frequency macroeconomic variables, when data
are available for a reasonably large number of countries or states. As many macroeconomic
variables have a stochastic trend, the forecasting methodology also addresses potentially
common stochastic trends. In this paper the particular focus is on forecasting annual real
GDP (Gross Domestic Product) growth rates in Africa.”

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Source: World Bank Global Economic Prospects (2022)

“The global recovery is set to decelerate markedly amid continued COVID-19 flare-ups, diminished policy support, and lingering supply bottlenecks. In contrast to that in advanced economies, output in emerging markets and developing economies (EMDEs) will remain substantially below the pre-pandemic trend over the forecast horizon. The global outlook is clouded by various downside risks, including renewed COVID-19 outbreaks due to Omicron or new virus variants, the possibility of de-anchored inflation expectations, and financial stress in a context of record-high debt levels. If some countries eventually require debt restructuring, this will be more difficult to achieve than in the past. Climate change may increase commodity price volatility, creating challenges for the almost two-thirds of EMDEs that rely heavily on commodity exports and highlighting the need for asset diversification. Social tensions may heighten as a result of the increase in between-country and within-country inequality caused by the pandemic. Given limited policy space in EMDEs to support activity if needed, these downside risks increase the possibility of a hard landing.

New paper by Jennifer L. Castle , Jurgen A. Doornik and David F. Hendry

“By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: very low frequency at 1000-year intervals for paleoclimate, through annual, monthly to intra-daily for current climate; weekly and daily for pandemic data; annual, quarterly and monthly for economic data, and seconds or nano-seconds in finance. Nevertheless, there are important commonalities to economic, climate and pandemic time series. First, time series in all three disciplines are subject to non-stationarities from evolving stochastic trends and sudden distributional shifts, as well as data revisions and changes to data measurement systems. Next, all three have imperfect and incomplete knowledge of their data generating processes from changing human behaviour, so must search for reasonable empirical modeling approximations. Finally, all three need forecasts of likely future outcomes to plan and adapt as events unfold, albeit again over very different horizons. We consider how these features shape the formulation and selection of forecasting models to tackle their common data features yet distinct problems.”

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“Economic forecasting is rarely easy. This is especially true in the current environment, as the relationship between economic activity and public health metrics such as the percentage of people vaccinated, or the number of COVID cases, remains far from predictable. 

Key macroeconomic questions remain. Is higher inflation likely to persist, or will it prove transitory? Will businesses be able to boost productivity despite the tight labor market, and supply chain disruptions? And what are some of the most useful metrics to assess economic recovery in the current environment? 

This week on EconoFact Chats, Julia Coronado discusses these questions, and offers her perspective on which metrics best indicate the health of the economy.”

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New Paper by Frank Schorfheide & Dongho Song

“We resuscitated the mixed-frequency vector autoregression (MF-VAR) developed in Schorfheide
and Song (2015, JBES) to generate macroeconomic forecasts for the U.S. during the COVID-19
pandemic in real time. The model combines eleven time series observed at two frequencies:
quarterly and monthly. We deliberately did not modify the model specification in view of the
COVID-19 outbreak, except for the exclusion of crisis observations from the estimation sample.
We compare the MF-VAR forecasts to the median forecast from the Survey of Professional
Forecasters (SPF). While the MF-VAR performed poorly during 2020:Q2, subsequent forecasts
were at par with the SPF forecasts. We show that excluding a few months of extreme
observations is a promising way of handling VAR estimation going forward, as an alternative of a
sophisticated modeling of outliers.”

Read more here.