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SSRN
In: Social science quarterly, Band 102, Heft 5, S. 2070-2087
ISSN: 1540-6237
AbstractObjectiveWe analyze the number of recorded cases and deaths of COVID‐19 in many parts of the world, with the aim to understand the complexities of the data, and produce regular forecasts.MethodsThe SARS‐CoV‐2 virus that causes COVID‐19 has affected societies in all corners of the globe but with vastly differing experiences across countries. Health‐care and economic systems vary significantly across countries, as do policy responses, including testing, intermittent lockdowns, quarantine, contact tracing, mask wearing, and social distancing. Despite these challenges, the reported data can be used in many ways to help inform policy. We describe how to decompose the reported time series of confirmed cases and deaths into a trend, seasonal, and irregular component using machine learning methods.ResultsThis decomposition enables statistical computation of measures of the mortality ratio and reproduction number for any country, and we conduct a counterfactual exercise assuming that the United States had a summer outcome in 2020 similar to that of the European Union. The decomposition is also used to produce forecasts of cases and deaths, and we undertake a forecast comparison which highlights the importance of seasonality in the data and the difficulties of forecasting too far into the future.ConclusionOur adaptive data‐based methods and purely statistical forecasts provide a useful complement to the output from epidemiological models.
In: Foundations and trends in econometrics volume 10, issue 3-4 (2020)
SSRN
Working paper
In: Palgrave Texts in Econometrics
In: Springer eBooks
In: Economics and Finance
In: Springer eBook Collection
Chapter 1: Introduction -- Chapter 2: Key Concepts: A Series of Primers -- Chapter 3: Why is the World Always Changing? -- Chapter 4: Making Trends and Breaks Work for us -- Chapter 5: Indicator Saturation Methods -- Chapter 6: Combining Theory and Data -- Chapter 7: Seeing into the Future -- Chapter 8: Conclusions
In: Palgrave Texts in Econometrics
This open access book focuses on the concepts, tools and techniques needed to successfully model ever-changing time-series data. It emphasizes the need for general models to account for the complexities of the modern world and how these can be applied to a range of issues facing Earth, from modelling volcanic eruptions, carbon dioxide emissions and global temperatures, to modelling unemployment rates, wage inflation and population growth. Except where otherwise noted, this book is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0.
In: Oxford review of economic policy, Band 34, Heft 1-2, S. 287-328
ISSN: 1460-2121
In: Economic Ideas You Should Forget, S. 77-78
In: Arne Ryde Memorial Lectures Series
In: Scottish journal of political economy: the journal of the Scottish Economic Society, Band 60, Heft 5, S. 523-525
ISSN: 1467-9485
In: Scottish journal of political economy: the journal of the Scottish Economic Society, Band 60, Heft 5, S. 495-522
ISSN: 1467-9485
Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointe