The Unassuming Economist

New post from livewire by JOEY MUI of Merlon Capital

“The problem with precision

Most forecasts begin with a starting point which is often anchored to current data. Forecasters tend to modestly extrapolate up or down from this level. This tendency to stick close to current conditions, or consensus views, limits a forecaster’s ability to comprehend the full range of possibilities or the impacts of more extreme circumstances.

Research by the International Monetary Fund explored the ability of economists to predict recessions between 1992 to 2014. It was a disaster. Economists consistently failed to predict a recession in GDP by a significant margin. Even as conditions deteriorated, economists stubbornly anchored their forecasts to the preceding non-recessionary period and adjusted their predictions downwards too little, too late.”

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New paper by Olivier Sprangers, Sebastian Schelter & Maartende Rijke

Abstract:

Probabilistic time series forecasting is crucial in many application domains, such as retail, ecommerce, finance, and biology. With the increasing availability of large volumes of data, a number of neural architectures have been proposed for this problem. In particular, Transformer-based methods achieve state-of-the-art performance on real-world benchmarks. However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models. To address this problem, we introduce a novel bidirectional temporal convolutional network that requires an order of magnitude fewer parameters than a common Transformer-based approach. Our model combines two temporal convolutional networks: the first network encodes future covariates of the time series, whereas the second network encodes past observations and covariates. We jointly estimate the parameters of an output distribution via these two networks. Experiments on four real-world datasets show that our method performs on par with four state-of-the-art probabilistic forecasting methods, including a Transformer-based approach and WaveNet, on two point metrics (sMAPE and NRMSE) as well as on a set of range metrics (quantile loss percentiles) in the majority of cases. We also demonstrate that our method requires significantly fewer parameters than Transformer-based methods, which means that the model can be trained faster with significantly lower memory requirements, which as a consequence reduces the infrastructure cost for deploying these models.

Read more here.

By Paul Beaudry & Tim Willems in AEJ: Macroeconomics

Abstract – Analyzing International Monetary Fund (IMF) data, we find that overly optimistic growth expectations for a country induce economic contractions a few years later. To isolate the causal effect, we take an instrumental variable approach—exploiting randomness in the country allocation of IMF mission chiefs. We first document that IMF mission chiefs differ in their individual degrees of forecast optimism, yielding quasi-experimental variation in the degree of forecast optimism at the country level. The mechanism appears to run through excessive accumulation of debt (public and private). Our findings illustrate the potency of unjustified optimism and underline the importance of basing economic forecasts upon realistic medium-term prospects.

Read more here.

By Philip Hans Franses and Max Welz

“This paper deals with forecasting low-frequency macroeconomic variables, when data
are available for a reasonably large number of countries or states. As many macroeconomic
variables have a stochastic trend, the forecasting methodology also addresses potentially
common stochastic trends. In this paper the particular focus is on forecasting annual real
GDP (Gross Domestic Product) growth rates in Africa.”

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Source: World Bank Global Economic Prospects (2022)

“The global recovery is set to decelerate markedly amid continued COVID-19 flare-ups, diminished policy support, and lingering supply bottlenecks. In contrast to that in advanced economies, output in emerging markets and developing economies (EMDEs) will remain substantially below the pre-pandemic trend over the forecast horizon. The global outlook is clouded by various downside risks, including renewed COVID-19 outbreaks due to Omicron or new virus variants, the possibility of de-anchored inflation expectations, and financial stress in a context of record-high debt levels. If some countries eventually require debt restructuring, this will be more difficult to achieve than in the past. Climate change may increase commodity price volatility, creating challenges for the almost two-thirds of EMDEs that rely heavily on commodity exports and highlighting the need for asset diversification. Social tensions may heighten as a result of the increase in between-country and within-country inequality caused by the pandemic. Given limited policy space in EMDEs to support activity if needed, these downside risks increase the possibility of a hard landing.