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.”

View Full-Text

“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.”

To know more click here.

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.

“Virtual live-stream only! Thursday, December 2nd, 2021, 12:30 pm – 2 pm ET: Neil R. Ericsson (Federal Reserve Board) will present “Evaluating the Federal Reserve’s Tealbook Forecasts”Co-authors: Maia Crook, Emilio J. Fiallos, J E. Seymour, Charlotte Singer, Ben Smith, François de Soyres.
Abstract: This paper examines publicly available Federal Reserve Board Tealbook forecasts of GDP growth for the United States and several foreign countries, focusing on potential time-varying biases and evaluating the Tealbook forecasts relative to other forecasts. Tealbook forecasts perform relatively well at short horizons, but with significant heterogeneity across countries. Also, while standard Mincer-Zarnowitz tests typically fail to detect biases in the Tealbook forecasts, recently developed indicator saturation techniques that employ machine learning are able to detect economically sizable and highly significant time-varying biases. Estimated biases differ not only over time, but by country and across the forecast horizon. These biases point to directions for forecast improvement. Chong and Hendry’s (1986) forecast-encompassing tests of the Tealbook forecasts relative to JP Morgan’s forecasts reveal distinct value added by each institution’s forecasts. For most countries and forecast horizons examined, each institution’s forecast can be improved by utilizing information from the other institution’s forecast.”

For more details read here.

From VoxEU.com by Maarten Verwey, Oliver Dieckmann, Przemyslaw Wozniak posted on 16 November 2021

“The growth outlook for the EU is for a continued economic expansion. With large shares of the population currently protected against severe COVID-19 cases and deaths (ECDC 2021), the EU economy is assumed to avoid lockdowns and to continue benefitting from the reopening momentum. As a result, the EU and the euro area, which were in the third quarter just a notch below their pre-pandemic output levels, are set to transition from recovery to expansion (Figure 1). This projection implies reaching the Commission’s extrapolated pre-pandemic forecast path within the forecast years, and approaching the pre-crisis growth trend much faster than after previous recessions. After the Global Financial Crisis in 2008-09, for example, it took the EU economy more than four years for just returning to the pre-crisis level of output. The atypical nature of the recession and the substantial policy support, including NGEU/RRF, were essential for this result.”

Continue reading here.