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43 records from EconBiz based on author Name
1. Tracking U.S. consumers in real time with a new Weekly Index of Retail Trade
abstractWe create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, small business revenue, mobility, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implement a novel benchmarking method that uses a mixed-frequency dynamic factor model to constrain the weekly index to match the monthly MRTS. We use the index to document several interesting features of U.S. retail sales during the Covid-19 pandemic, many of which are not visible in the MRTS. In addition, we show that our index would have more accurately predicted the MRTS in real time during the pandemic when compared to either consensus forecasts available at the time, monthly autoregressive models, or other high-frequency data that attempts to track consumer spending. The gains are substantial, with roughly 60 to 80 percent reductions in mean absolute forecast errors.
Brave, Scott A.; Fogarty, Michael; Aaronson, Daniel; Karger, Ezra; Krane, Spencer David;2021
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability:
Link Link Citations: 1 (based on OpenCitations)
2. Charged and almost ready : what is holding back the resale market for battery electric vehicles?
abstractWe utilize vehicle registration microdata for all new and used vehicles registered in the U.S. for model years 2010-2022 to study the market for used battery electric vehicles (BEVs). From these records, we establish two stylized facts: 1) BEVs enter the used market at the slowest rate compared to any other powertrain technology, and 2) BEVs are driven significantly less than vehicles featuring other powertrain technologies. We connect these facts through a statistical model of used vehicle registration counts and find that there are significant behavioral differences between BEV and other new vehicle owners in how utilization (both on average and at the margin) leads to these vehicles being resold. By way of a counterfactual exercise that equalizes average vehicle miles traveled, we then illustrate that these behavioral differences can explain from 10-30 percent of the differential rates of transition from new to used vehicle status we observe between BEVs and internal combustion engine (ICE) vehicles.
Bognar, Levi; Brave, Scott A.; Klier, Thomas H.; McGranahan, Leslie;2023
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
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Link Link 3. Charged and almost ready: What is holding back the resale market for battery electric vehicles?
Bognar, Levi; Brave, Scott A.; Klier, Thomas H.; McGranahan, Leslie;2023
Type: Working Paper;
Availability:
Link 4. Using the eye of the storm to predict the wave of Covid-19 UI claims
abstractWe leverage an event-study research design focused on the seven 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.
Aaronson, Daniel; Brave, Scott A.; Butters, R. Andrew; Sacks, Daniel; Seo, Boyoung;2020
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability:
Link Link Citations: 3 (based on OpenCitations)
5. Using the eye of the storm to predict the wave of Covid-19 UI claims
Aaronson, Daniel; Brave, Scott A.; Butters, R. Andrew; Sacks, Daniel W.; Seo, Boyoung;2020
Type: Aufsatz in Zeitschrift; Article in journal;
Availability:
6. The perils of working with Big Data and a SMALL framework you can use to avoid them
abstractThe use of "Big Data" to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages when compared to these more traditional data sources, but with these advantages also comes the potential for pitfalls. We lay out a framework called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL framework should help users of big data spot concerns in their own work and that of others who rely on such data to draw conclusions with actionable public policy or business implications. To demonstrate, we provide several case studies that show a healthy dose of skepticism can be warranted when working with and interpreting these new big data sources.
Brave, Scott A.; Butters, R. Andrew; Fogarty, Michael;2020
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability:
Link Link 7. Predicting benchmarked US state employment data in realtime
abstractUS payroll employment data come from a survey of nonfarm business establishments and are therefore subject to revisions. While the revisions are generally small at the national level, they can be large enough at the state level to substantially alter assessments of current economic conditions. Researchers and policymakers must therefore exercise caution in interpreting state employment data until they are "benchmarked" against administrative data on the universe of workers some 5 to 16 months after the reference period. This paper develops and tests a state space model that predicts benchmarked US state employment data in realtime. The model has two distinct features: 1) an explicit model of the data revision process and 2) a dynamic factor model that incorporates realtime information from other state-level labor market indicators. We find that across the 50 US states, the model reduces the average size of benchmark revisions by about 9 percent. When we optimally average the model’s predictions with those of existing models, we find that we can reduce the average size of the revisions by about 15 percent.
Brave, Scott A.; Gascon, Charles; Kluender, William; Walstrum, Thomas;2019
Type: Graue Literatur; Non-commercial literature; Arbeitspapier; Working Paper;
Availability: Link Link Link
8. Predicting benchmarked US state employment data in real time
Gascon, Charles S.; Brave, Scott A.; Kluender, William; Walstrum, Thomas;2019
Type: Arbeitspapier; Working Paper;
Availability:
Link 9. The perils of working with big data, and a SMALL checklist you can use to recognize them
Brave, Scott A.; Butters, R. Andrew; Fogarty, Michael;2022
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
10. Forecasting unemployment insurance claims in realtime with Google Trends
Aaronson, Daniel; Brave, Scott A.; Butters, R. Andrew; Fogarty, Michael; Sacks, Daniel W.; Seo, Boyoung;2022
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 14 (based on OpenCitations)