The Unassuming Economist

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. “

Read more here.

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.

New NBER Working paper by Nicholas Bloom, Takafumi Kawakubo, Charlotte Meng, Paul Mizen, Rebecca Riley, Tatsuro Senga & John Van Reenen.

“We link a new UK management survey covering 8,000 firms to panel data on productivity in manufacturing and services. There is a large variation in management practices, which are highly correlated with productivity, profitability and size. Uniquely, the survey collects firms’ micro forecasts of their own sales and also macro forecasts of GDP. We find that better managed firms make more accurate micro and macro forecasts, even after controlling for their size, age, industry and many other factors. We also show better managed firms appear aware that their forecasts are more accurate, with lower subjective uncertainty around central values. These stylized facts suggest that one reason for the superior performance of better managed firms is that they knowingly make more accurate forecasts, enabling them to make superior operational and strategic choices.”

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New paper by Tianyi Wang, Hong Yan, Zhuo Huang & Fang Liang in Economic Modelling.

“In this paper, we develop a new model, the Realized GARCH-RSRK, to determine the time-varying distribution of financial returns with realized higher moments. Based on Gram-Charlier expansion (GCE) density, we first explicitly link the expansion parameters with moments that are calculated based on intraday returns using our new model. Then, the Cornish-Fisher expansion is applied to forecast Value-at-Risk (VaR) with estimated moments to demonstrate the economic significance of this new model. Compared with the daily-return-based dynamic higher moments models, the inclusion of realized higher moments significantly improves this model’s ability to forecast extreme tails. The empirical results indicate that this new model outperforms the benchmark models when forecasting extreme VaR. In addition, we provide a formula to correct the moments associated with the commonly used squared transformation of GCE. Our empirical evidence highlights the importance of using corrected moments in VaR forecasting.”

Read more by clicking here.

New article by John Cochrane from John Cochrane’s blog.

“Torsten Slok, chief economist at Apollo Global Management, passes along the above gorgeous graph. Fed forecasts of interest rates behave similarly. So does the “market forecast” embedded in the yield curve, which usually slopes upward.
Torsten’s conclusion:

The forecasting track record of the economics profession when it comes to 10-year interest rates is not particularly impressive, see chart [above]. Since the Philadelphia Fed started their Survey of Professional Forecasters twenty years ago, the economists and strategists participating have been systematically wrong, predicting that long rates would move higher. Their latest release has the same prediction.

Well. Like the famous broken clock that is right twice a day, note the forecasts are “right” in times of higher rates. So don’t necessarily run out and buy bonds today.

Can it possibly be true that professional forecasters are simply behaviorally dumb, refuse to learn, and the institutions that hire them refuse to hire more rational ones?”

To read more click here.