The Unassuming Economist

From a paper by Luciano Vereda, Helder Ferreira de Mendonça, and George Morcerf:

“Our study advances the modelling of forecast revisions by accounting for the nuanced impact of informational shocks across different time horizons. Specifically, we introduce modifications to the error structure of regression models used to detect biases in macroeconomic forecasts. Drawing on consensus forecasts of inflation and output growth from the central banks of Brazil, Chile, and Mexico, our approach offers a nuanced understanding of bias estimation uncertainty, leading to a more robust rejection of the null hypothesis of no biases. By elucidating the differential effects of informational shocks on forecast accuracy across time periods, our findings not only contribute to the refinement of forecasting methodologies but also have implications for policymakers and economic analysts striving for more accurate and reliable predictions in dynamic economic environments.”

From Mark Perry (AEI):

“Tomorrow is Earth Day 2022 and marks the 52nd anniversary of Earth Day, so it’s time for my annual CD post on the spectacularly wrong predictions that were made around the time of the first Earth Day in 1970…..

In the May 2000 issue of Reason Magazine, award-winning science correspondent Ronald Bailey wrote an excellent article titled “Earth Day, Then and Now: The planet’s future has never looked better. Here’s why” to provide some historical perspective on the 30th anniversary of Earth Day. In that article, Bailey noted that around the time of the first Earth Day in 1970, and in the years following, there was a “torrent of apocalyptic predictions” and many of those predictions were featured in his Reason article. Well, it’s now the 51st anniversary of Earth Day, and a good time to ask the question again that Bailey asked 21 years ago: How accurate were the predictions made around the time of the first Earth Day in 1970? The answer: “The prophets of doom were not simply wrong, but spectacularly wrong,” according to Bailey. Here are 18 examples of the spectacularly wrong predictions made around 1970 when the “green holy day” (aka Earth Day) started:

1. Harvard biologist George Wald estimated that “civilization will end within 15 or 30 years [by 1985 or 2000] unless immediate action is taken against problems facing mankind.”

2. “We are in an environmental crisis that threatens the survival of this nation, and of the world as a suitable place of human habitation,” wrote Washington University biologist Barry Commoner in the Earth Day issue of the scholarly journal Environment.

3. The day after the first Earth Day, the New York Times editorial page warned, “Man must stop pollution and conserve his resources, not merely to enhance existence but to save the race from intolerable deterioration and possible extinction.”

4. “Population will inevitably and completely outstrip whatever small increases in food supplies we make,” Paul Ehrlich confidently declared in the April 1970 issue of Mademoiselle. “The death rate will increase until at least 100-200 million people per year will be starving to death during the next ten years [by 1980].”

5. “Most of the people who are going to die in the greatest cataclysm in the history of man have already been born,” wrote Paul Ehrlich in a 1969 essay titled “Eco-Catastrophe! “By…[1975] some experts feel that food shortages will have escalated the present level of world hunger and starvation into famines of unbelievable proportions. Other experts, more optimistic, think the ultimate food-population collision will not occur until the decade of the 1980s.”

Continue reading here.

New post by Timothy Taylor on Conversable Economist posted on 17th February.

“The question of whether a burst of inflation turn into permanent inflation should depend, at least in part, on expectations about inflation. If workers and firms expect higher inflation, then the workers are more likely to press for higher wages to compensate–and firms are more likely to be amenable to such increases. An inflationary cycle can emerge where expectations of higher inflation lead to more price and wage increases, and those price and wage increases lead to higher inflation.”

Read more by clicking here.

New VoxEU.org post by Laura Coroneo, Fabrizio Iacone, Alessia Paccagnini, Paulo Santos Monteiro published on 16 February 2022.

“For policymakers and healthcare providers, prediction of the evolution of an epidemic is extremely important (Manski 2020, Castle et al. 2020). Timely and reliable projections are required to assist health authorities, and the community in general, in coping with an infection surge and to inform public health interventions such as enforcing (or facilitating) local or national lockdowns (Heap et al. 2020). Weekly forecasts of the evolution of the COVID-19 pandemic generated by various independent institutions and research teams have been collected by the Centers for Disease Control and Prevention (CDC) in the US.¹ These forecasts are intended to inform the decision-making process for public health interventions by predicting the impact of the COVID-19 pandemic for up to four weeks. However, this wealth of forecasts also poses a problem: how to act when confronted with heterogeneous forecasts and, in particular, how to select the most reliable projections.

Forecasting teams, methods, and assumptions

The forecasting teams include data scientists, epidemiologists, and statisticians. They use different methodologies and approaches (e.g. the susceptible-exposed-infectious-recovered (SEIR), Bayesian, and deep learning models) and combine a range of data sources and assumptions concerning the impact of non-pharmaceutical interventions on the spread of the epidemic (such as social distancing and the use of face coverings). In Table 1, we report the eight teams that continuously submitted their predictions since the start of the pandemic, for the period 20 June 2020 to 20 March 2021. “

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New paper by YaojieZhang, M.I.M.Wahab & YudongWang in International Journal of Forecasting.

“This paper aims to improve the predictability of aggregate oil market volatility with a substantially large macroeconomic database, including 127 macro variables. To this end, we use machine learning from both the variable selection (VS) and common factor (i.e., dimension reduction) perspectives. We first use the lasso, elastic net (ENet), and two conventional supervised learning approaches based on the significance level of predictors’ regression coefficients and the incremental R-square to select useful predictors relevant to forecasting oil market volatility. We then rely on the principal component analysis (PCA) to extract a common factor from the selected predictors. Finally, we augment the autoregression (AR) benchmark model by including the supervised PCA common index. Our empirical results show that the supervised PCA regression model can successfully predict oil market volatility both in-sample and out-of sample. Also, the recommended models can yield forecasting gains in both statistical and economic perspectives. We further shed light on the nature of VS over time. In particular, option-implied volatility is always the most powerful predictor.”

Read more here.