The Unassuming Economist

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.”

From The Conference Board.

By Dana M. Peterson & Lynn Franco

“US consumer expectations as measured by The Conference Board Consumer Confidence Index ticked up in October, but this followed three months of declines. Did the declines signal recession in 2022 or just a hiccup related to the Delta variant? We propose the latter.

Indeed, material downshifts in the consumer expectations gauge, with the exception of the pandemic, have preceded US recessions. However, closer examination of the index reveals at least 18 instances since the inception of the measure when there were 10 point or more declines in the index that did not predict recession (Figure 1). Notably, those dips often coincided with shocks to the economy, including wars, bad weather, and happenings in Washington, DC (Figure 2). Indeed, the three month decline in expectations this year occurred while the Delta variant swept across the nation – a sort of shock within the pandemic shock. Notably, consumer expectations were rising earlier this year as vaccinations rose, mobility restrictions lessened, and in-person services began to reopen.”

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From https://voxeu.org/

By Elena Bobeica, Gabriel Pérez-Quirós, Gerhard Rünstler, Georg Strasserposted on 31 October 2021 

“The recent decade has shown that forecasters need to continuously adapt their tools to cope with increasing macroeconomic complexity. Just like the global crisis, the current Covid-19 pandemic highlights once again that forecasters cannot be content with just assessing the single most likely future outcome – such as a single number for future GDP growth in a certain year. Instead, a characterisation of all possible outcomes (i.e. the entire distribution) is necessary to understand the likelihood and nature of extreme events.

This is key for central bank forecasters as well, as pointed out by ECB Executive Board member Philip Lane in his opening remarks at the 11th Conference on Forecasting Techniques. Central banks rely heavily on forecasts to design their policy and need robust techniques to navigate through turbulent times. They not only ensure price stability and are thus directly interested in the most likely future inflation path, but in the process also contribute to the understanding, managing, and handling of macro-economic risks and thus need to grasp the likelihood of extreme events (see also the discussion in Greenspan 2004).”

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