Due to its very specific nature and its worldwide effects, the COVID-19 pandemic has raised many challenges for economic forecasters. Almost every aspect of economic forecasting is concerned, such as measuring the unprecedented health shock, lack of reliable economic data, or massive spikes in COVID-induced uncertainties. This virtual workshop brings together researchers and policy makers working on innovative approaches of modelling, evaluation and forecasting of the pandemic and its impact on the economy.
Please register to join the workshops on Zoom.
Xuguang Simon Sheng (American University)
Laurent Ferrara (SKEMA Business School)
Please see expandable Session 1-4 sections below the daily overviews for abstracts and presenter details. All times Eastern Daylight Time (EDT).
July 6 Overview
Moderator: Xuguang Simon Sheng (American University)
- 9:00 – 9:10 EDT
Max Paul Friedman (Dean, College of Arts and Science, American University)
- 9:10 – 10:40 EDT
Nowcasting Tail Risks to Economic Activity with Many Indicators
Andrea Carriero (Queen Mary University of London), Todd Clark (Cleveland Fed), and Massimiliano Marcellino (Bocconi University)
Nowcasting Norwegian Household Consumption with Debit Card Transaction Data
Knut Are Aastveit, Tuva Marie Fastbø, Eleonora Granziera, Kenneth Sæterhagen Paulsen, and Kjersti Næss Torstensen (all Norges Bank)
Real-time High-frequency Monitoring of Growth-at-Risk
Laurent Ferrara (SKEMA Business School), Matteo Mogliani (Banque de France), and Jean-Guillaume Sahuc(Banque de France)
- 10:40 – 11:00 EDT
- 11:00 – 12:00 EDT
Impact of COVID-19 on Firm Inflation Expectations and Uncertainty
Brent Meyer (Atlanta Fed), Nicholas Parker (Atlanta Fed), and Xuguang Simon Sheng (American University)
The Causal Effect of Information about COVID-19 on People’s Assessment of the Economy, the Government, and Their Personal Life
Dzung Bui (University of Marburg), Lena Drager (Leibniz University of Hannover), Bernd Hayo (University of Marburg), and Giang Nghiem (Leibniz University of Hannover)
July 7 Overview
Moderator: Laurent Ferrara, SKEMA Business School
- 9:00 – 10:30 EDT
State Revenue Forecasting in the Aftermath of COVID-19 Shutdown using Machine Learning
Kajal Lahiri and Cheng Yang (all University at Albany, SUNY)
Forecasting the Covid-19 Recession and Recovery: Lessons from the Financial Crisis
Claudia Foroni (European Central Bank), Massimiliano Marcellino (Bocconi University), and Dalibor Stevanovic (Université du Québec à Montréal)
Back to the Present: Learning about the Euro Area through a Now-casting Model
Danilo Cascaldi-Garcia (Federal Reserve Board), Thiago Ferreira (Federal Reserve Board), Domenico Giannone (Amazon.com), and Michele Modugno (Federal Reserve Board)
- 10:30 – 10:50 EDT
- Coffee Break
- 10:50 – 11:50 EDT
Pandemics, Huricanes, and (Google) Trends, UI:
Forecasting Unemployment Insurance During Covid-19
Daniel Aaronson (Chicago Fed), Scott Brave (Chicago Fed), Andrew Butters (Indiana University), Daniel Sacks (Indiana University), and Boyoung Seo (Indiana University)
Panel Forecasts of Country-Level Covid-19 Infections
Laura Liu (Indiana University), Hyungsik Roger Moon (University of Southern California), and Frank Schorfheide (University of Pennsylvania)
- 11:50 – 12:00 EDT
Todd Clark (Cleveland Fed)
Nowcasting -- predicting current quarter GDP growth -- is commonly viewed as an important and unique forecasting problem. Much of the nowcasting literature has focused on data available at a monthly and quarterly frequency. In 2020, the shutdown of significant portions of the economy to restrain the outbreak of the coronavirus has raised practical interest in high-frequency indicators of economic activity in the US and other economies. Apart from nowcasting considerations, a rapidly growing body of research has examined tail risks in macroeconomic outcomes, typically at a horizon of one quarter or one year ahead. Most of this work has focused on the risks of significant declines in GDP, and has relied on quantile regression methods to estimate tail risks.
Building on these strains of work, this paper assesses the ability of models to produce accurate tail risk nowcasts of GDP growth with a potentially wide array of information. We consider not only different models but also different methods for data reduction. Our starting point is a mixed frequency regression setup, in which, for nowcasting GDP growth within a quarter, each time series of monthly indicators is transformed into three quarterly time series, each containing observations for, respectively, the first, second, or third month of the quarter. At the moment in time that the forecast is formed, the model includes only the quarterly series without missing observations, which addresses the ragged edge of the data. Bayesian methods are used to estimate the model, which facilitates providing shrinkage on estimates of a model that can be quite large.
Our paper makes three broad contributions. First, we extend the research literature's typical forecast calendar setup to use 15 different weeks as forecast origins for a quarter's nowcast, rather than a few months. This setup permits an assessment of the evolution of forecasts and tail risks with the week-by-week flow of information in the quarter. Second, we consider higher frequency data, making use of a number of indicators at a weekly frequency and not just monthly indicators. Finally, we examine nowcasts of tail risks to economic activity, focused on the 5 percent quantile.
Our results show that, within some limits, more information improves the accuracy of tail risk forecasts. Forecast accuracy typically improves as time moves forward from week to week within a quarter, making additional data available. In a given week, large models often forecast as well as or better than small models. There is a clear benefit to adding a base set of financial indicators to the base set of macro indicators. Adding other weekly indicators of economic activity doesn't have much effect on forecast accuracy, either to help or harm. Among the models or estimation approaches we consider, our regression with stochastic volatility and our Bayesian quantile regression perform consistently, offering solid gains in tail risk forecast accuracy. Data reduction via factors and forecast averaging can also help forecast accuracy.
Eleonora Granziera (Norges Bank)
The recent shutdown of significant portions of the worldwide economy, in order to restrain the outbreak of the coronavirus, has triggered a global recession. The uncertain consequences of the rapid spread of the virus and the induced infection control measures have made it extremely challenging for forecasters and policymakers to quantify and assess the current and future outlook of the economy. This has raised a renewed interest in the search for reliable high-frequency indicators that can track the real economy in a timely matter.
In this paper, we document that debit card transaction data serve as an early and reliable indicator for household consumption in Norway. We use a novel data set covering all debit card transactions for Norwegian households to nowcast quarterly household consumption in Norway. These card payments data are free of sampling errors and are available without delay, and currently account for more than 35% of the total value of all household consumption expenditures. Therefore, they prove a valuable early indicator of household spending.
To account for mixed-frequency data, we estimate various mixed-data sampling (MIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4-2020Q1. Our results show that MIDAS regressions with credit card transaction data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high-frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a nowcast of the first quarter of 2020, a quarter characterized by heightened uncertainty due to the COVID-19 pandemic. For this quarter the MIDAS model with BankAxept data successfully predicts the magnitude of the drop in consumption, while the other models forecast a much milder decline.
Matteo Mogliani (Banque de France)
Monetary policy decisions require a real-time assessment of different economic scenarios, including the most extreme ones. In this paper, we propose an extension of the Growth-at-Risk approach that assesses financial risks on GDP growth, by accounting for the high-frequency nature of financial conditions. In this respect, we propose a Mixed-Data Sampling (MIDAS) approach of the quantile regression of quarterly GDP growth rate on daily measures of financial conditions, using a recently developed Financial Conditions Index for the euro area. Overall, we show that financial risks on euro area economic growth can be monitored in real-time on a high-frequency basis using our approach. This high-frequency monitoring turns out to be useful for policy-makers, especially during crisis times.
Brent Meyer (Atlanta Fed)
Utilizing unique firm-level survey information from the Atlanta Fed’s survey of Business Inflation Expectations, this paper investigates the impact that COVID-19 (and the attendant measures to stem its spread) are having on firm’s probabilistic inflation expectations, subjective inflation uncertainty, and price change expectations. We also examine other, policy relevant and related impacts to sales activity and supply chain disruption. While tentative, our results suggest that the COVID-19 shock is having a dramatic impact on year-ahead inflation expectations and uncertainty, but much less so for longer-run expectations. We also find suggestive evidence supporting the notion that the demand-shock elements of COVID-19 are outweighing the supply-shock aspect of the pandemic.
Lena Drager (Leibniz University of Hannover)
This paper evaluates the causal effect of information treatments about COVID-19 in a new survey on households in Thailand and Vietnam. Vietnam and Thailand were chosen, because they are both emerging economies from the same region, but other population surveys indicated very different beliefs about how the respective government is handling the COVID-19 crisis.
We randomly split up our samples for each country and apply four different information treatments in addition to a control group. First, a map presenting the share of people in a country stating that they perceive the government’s reaction as insufficient and some text. This treatment is very different for Thailand and Vietnam, as Thailand is at the very top of countries reporting the worst assessment of their government, while Vietnam is the country with the highest agreement with its government policies. In the second treatment, respondents receive information based on a global survey on how people judge the public's response to the COVID-19 pandemic. Here, the treatment effect is similar for both Thailand and Vietnam. Third, respondents receive information on the International Labour Organization’s devastating forecasts about the effects of COVID-19 on worldwide employment. Fourth, respondents are shown a graph and some text illustrating the effectiveness of social distancing for avoiding COVID-19 infections.
The results suggest that the treatment effects of our information experiment on trust in the government, individual concerns related to the COVID-19 crisis and on life satisfaction are generally quite small, while we find larger effects on macroeconomic expectations, consumer sentiments and the marginal propensity to consume. Among the four treatments, the ILO treatment tends to have the largest effect. In addition, treatment effects tend to be larger in the Thai survey compared to the Vietnamese data. Our survey also reveals a large degree of disagreement with the government’s policy reaction to COVID-19, which is not present in the Vietnamese data. Therefore, larger treatment effects of additional information provided by our survey might reflect this disagreement.
Kajal Lahiri (University at Albany, SUNY)
Given the balance budget rule, good revenue forecasts are critical for state policy makers to decide on their expenditure plans for the year. We develop a MIDAS model for revenue forecasting characterized by arbitrary patterns of missing data and machine learning. Since recessions and policy changes generate big errors in revenue forecasts, we pay special attention to various leading indicators for recessions and newly enacted tax policies. We forecast yearly tax revenues using monthly tax receipts and also two dynamic factors extracted from two large sets of monthly and quarterly indicators specific to the New York State and the U.S. economy. These three models are combined with optimally to generate monthly multi-period forecasts with bootstrapped uncertainty fan charts. To coincide with the budget process, our forecasts start at the 18-month horizon, and are continuously updated till the end of the fiscal year. The sample covers fiscal years 1986-2020, and data till 2007 is used in estimation to evaluate out-of-sample forecasts over 2008-2020. Our model successfully predicted the massive revenue shortfalls during the 2007-09 recession and also during FY 2019 following the Federal Tax Cuts and Jobs Act of 2017.
The pandemic caused by the novel coronavirus outbreak and the economic shut down has dramatically upended state tax revenue forecasts for FY 2021.With updated March data, the boosting forecasts for FY 2021 is revised nominally to -4.39% from an initial estimate of 2.86% with data up January. As new data will be released over the year, our model is uniquely poised to forecast future tax receipts in real time to aid appropriate expenditure decisions during the rest of the fiscal year.
Dalibor Stevanovic (Université du Québec à Montréal)
We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track by a specific form of intercept correction. Among all these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and following recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment and very persistent negative effects on trend growth. Similar findings emerge also for the other G7 countries.
Danilo Cascaldi-Garcia Danilo Cascaldi-Garcia (Federal Reserve Board)
We construct a model to provide GDP forecasts for the euro area and the three largest member countries---Germany, France and Italy. We show that our model improves significantly the accuracy of forecasts for all of these economies. Our model processes the large flow of data released continuously and asynchronously for these economies while accounting for their short-term heterogeneous dynamics. We evaluate the ability of our model to distill the large flow of data by conducting a real-time forecasting evaluation and by focusing on key specific episodes: the Global Financial Crisis, the European sovereign debt crisis, and the onset of the Great Lockdown.
Andrew Butters (Indiana University)
How best to leverage the growing availability of high frequency and granular micro data, like Google Trends, to inform the forecasts of key economic activity indicators is the focus of much research, especially since the Covid-19 pandemic. A primary hurdle with these sorts of data sources, however, is finding credible ways to identify the key elasticities of interest for prediction. We leverage an event-study research design focused on the costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic “unemployment,” we show that out-of-sample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty surrounding these estimates and demonstrate their use within a broader forecasting framework for US economic activity.
Laura Liu (Indiana University)
Our project contributes to the rapidly growing research on generating forecasts related to the current COVID-19 pandemic. We are adapting forecasting techniques for panel data to predict the smoothed daily number of active COVID-19 infections for a cross-section of approximately one hundred countries/regions. The data are obtained from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.