The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Should Macroeconomic Forecasters Use Daily Financial Data and How?
Abstract
We introduce easy-to-implement, regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of mixed data sampling (MIDAS) regressions. We also extract a novel small set of daily financial factors from a large panel of about 1000 daily financial assets. Our analysis is designed to elucidate the value of daily financial information and provide real-time forecast updates of the current (nowcasting) and future quarters of real GDP growth.
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Forecasting, Structural Time Series Models and the Kalman Filter
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Determining the Number of Factors in Approximate Factor Models
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Forecasting Using Principal Components From a Large Number of Predictors
Stock, J. H., & Watson, M. W. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502388618960 · Cited by 2,985
The federal funds rate and the channels of monetary transmissionPreprint
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Macroeconomic Forecasting Using Diffusion Indexes
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Approximately normal tests for equal predictive accuracy in nested models
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Cited by
Now-Casting and the Real-Time Data Flow
Bańbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2013) · Handbook of Economic Forecasting · doi:10.1016/b978-0-444-53683-9.00004-9 · Cited by 448
Metadata
- Authors
- Andreou, E., Ghysels, E., & Kourtellos, A.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2013-04
- Volume
- 31
- Issue
- 2
- Pages
- 240-251
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2155963925
- Cited by
- 331
- References
- 81
- FWCI
- 36.27
- Type
- journal article
- Language
- en
- Open access
- closed
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- Andreou, E., Ghysels, E., & Kourtellos, A. (2013). Should Macroeconomic Forecasters Use Daily Financial Data and How? Journal of Business & Economic Statistics. https://doi.org/10.1080/07350015.2013.767199
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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