Call for Papers:
Special Issue on High-Frequency Data in Economics
Following the COVID-19 pandemic, the importance of high-frequency data for monitoring economic activity increased significantly. In the past, high-frequency data was largely confined to financial markets, such as stock prices, exchange rates, and interest rates. However, the rapid and unprecedented impact of the pandemic on global economies made it imperative to assess economic developments at high frequency to support timely policymaking. In this context, sourcing new data became crucial to address the delays and lower frequency of traditional economic indicators.
Interest in high-frequency data has only grown since the pandemic, expanding well beyond its initial use for tracking real-time economic activity. Researchers are increasingly leveraging this data for a wider range of applications, including inflation analysis, event studies, and other areas of economic research. This growing body of literature highlights the versatility and value of high-frequency data in addressing complex and time-sensitive economic questions.
While high-frequency data presents methodological challenges, it also opens up opportunities for innovative research and fresh insights. This special issue seeks to explore new methods and applications of high-frequency data in economics.
Submission Guidelines
Submissions will be evaluated through the Portuguese Economic Journal (PEJ) standard review process. For specific submission guidelines, please visit the journal's page on the Springer website and select 'special issue' as the article type when submitting. Authors are also encouraged to review previous PEJ special issues for reference.
Deadline for submissions
Submissions to this Special Issue are welcome from 15 October 2024 to 15 April 2025. Preliminary versions of selected papers may be presented in a special session at the 18th Annual Meeting of the PEJ, to be held from 4 to 6 July 2025 in Lisbon, Portugal. The Special Issue will be published in 2026.
Special issue editors
- Caballo, Alberto (Harvard Business School)
- Rua, António (INE - Statistics Portugal & Nova SBE)
