The Unassuming Economist

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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From a new CESifo working paper by Kajal Lahiri & Cheng Yang

“We forecast New York state tax revenues with a mixed-frequency model using a number of machine learning techniques. We found boosting with two dynamic factors extracted from a select list of New York and U.S. leading indicators did best in terms of correctly updating revenues for the fiscal year in direct multi-step out-of-sample forecasts. These forecasts were found to be informationally efficient over 18 monthly horizons. In addition to boosting with factors, we also studied the advisability of restricting boosting to select the most recent macro variables to capture abrupt structural changes. Since the COVID-19 pandemic upended all government budgets, our boosted forecasts were used to monitor revenues in real time for the fiscal year 2021. Our estimates showed a drastic year-over-year decline in real revenues by over 16% in May 2020, followed by several upward nowcast revisions that led to a recovery to -1% in March 2021, which was close to the actual annual value of -1.6%.”