Turning points and classification
Economic time-series data is commonly categorized into a discrete number of persistent regimes. I survey a variety of approaches for real-time prediction of these regimes and the turning points between them, where these predictions are formed in a data-rich environment. I place particular emphasis o...
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| Veröffentlicht in: | Macroeconomic forecasting in the era of big data |
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| 1. Verfasser: | |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
2020
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Tag hinzufügen | Zusammenfassung: | Economic time-series data is commonly categorized into a discrete number of persistent regimes. I survey a variety of approaches for real-time prediction of these regimes and the turning points between them, where these predictions are formed in a data-rich environment. I place particular emphasis on supervised machine learning classification techniques that are common to the statistical classification literature, but have only recently begun to be widely used in economics. I also survey Markov-switching models, which are often used for unsupervised classification of economic data. The approaches surveyed are computationally feasible when applied to large datasets, and the machine learning algorithms employ regularization and cross-validation to prevent overfitting in the face of many predictors. A subset of the approaches conduct model selection automatically in forming predictions. I present an application to real-time identification of US business cycle turning points based on a wide dataset of 136 macroeconomic and financial time series. |
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| ISBN: | 9783030311490 303031149X |