The Unassuming Economist

“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.”

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From Econbrowswer –

“Chinn and Coibion (2014) and subsequent analyses find futures do a fairly good job at prediction. Chinn and Coibion examined data up to 2012, for WTI, while Kwas and Rubszek (Forecasting, 2021) examined both WTI and Brent for 2000-March 2021. As noted in this post, futures improve upon a random walk for both RMSFE and direction of change at horizons up to a year.”

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New IMF Working paper by Yan Carrière-Swallow and José Marzluf

“We analyze the causes of the apparent bias towards 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 program, 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.”

New paper in Journal of Econometrics posted by Jacopo Cimadomo, Domenico Giannone, Michele Lenza, Francesca Monti & Andrej Sokold.

“Monitoring economic conditions in real time, or nowcasting, and Big Data analytics share some challenges, sometimes called the three “Vs”. Indeed, nowcasting is characterized by the use of a large number of time series (Volume), the complexity of the data covering various sectors of the economy, with different frequencies and precision and asynchronous release dates (Variety), and the need to incorporate new information continuously and in a timely manner (Velocity). In this paper, we explore three alternative routes to nowcasting with Bayesian Vector Autoregressive (BVAR) models and find that they can effectively handle the three Vs by producing, in real time, accurate probabilistic predictions of US economic activity and a meaningful narrative by means of scenario analysis.”

Continue reading here.