The Unassuming Economist

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.”

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A new paper by Le HaThua & RobertoLeon-Gonzalezb

“Forecasting macroeconomic variables in rapidly changing emerging economies presents a number of challenges. In addition to structural changes, the time-series data are usually available only for a short number of periods, and predictors are available in different lengths and frequencies. Dynamic model averaging (DMA), by allowing the forecasting model to change dynamically over time, permits the use of predictors with different lengths and frequencies for the purpose of forecasting in a rapidly changing economy. This study uses DMA to forecast inflation and growth in Vietnam, Thailand, Philippines, Sri Lanka and Ghana. We compare its forecasting performance with a wide range of other time-series methods. We find that the size and composition of the optimal predictor set changed, indicating changes in the economic relationships over time. We also find that DMA frequently produces more accurate forecasts than other forecasting methods for both inflation and economic growth in the countries studied.”

New Paper by Ritong Quc, Allan Timmermanna & Yinchu Zhub

“This paper develops new methods for pairwise comparisons of predictive accuracy with cross-sectional data. Using a common factor setup, we establish conditions on cross-sectional dependencies in forecast errors which allow us to test the null of equal predictive accuracy on a single cross-section of forecasts. We consider both unconditional tests of equal predictive accuracy as well as tests that condition on the realization of common factors and show how to decompose forecast errors into exposures to common factors and idiosyncratic components. An empirical application compares the predictive accuracy of financial analysts’ short-term earnings forecasts across six brokerage firms.”