Data-Driven Models
Data-Driven Models
Empirical forecasting models · Sources
Primary papers, model variants, source notes, and review signals behind the Dynamic factor model page.
First-party releases, central-bank materials, official statistical agencies, and institutional documents.
[S1] European Central Bank
Doz, Giannone, and Reichlin (2011) 'A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering': provided the state-space estimation framework used by the ECB and NY Fed.
Primary - European Central Bank
Peer-reviewed papers, books, and research used to ground model mechanisms or contested interpretations.
[S2] Journal of the American Statistical Association
Forecasting Using Principal Components from a Large Number of Predictors
Stock and Watson on factor forecasting.
Academic - Journal of the American Statistical Association - dated 2002
[S3] Econometrica
Determining the Number of Factors in Approximate Factor Models
Bai and Ng on factor-number selection.
Academic - Econometrica - dated 2002
Reference material used for orientation; read primary and academic sources first when claims conflict.
[S4] Reference
Stock and Watson (2002a) 'Forecasting Using Principal Components from a Large Number of Predictors': demonstrated that principal component factors from a large panel improve forecasts of individual macro variables across the board.
Reference
[S5] Reference
Stock and Watson (2002b) 'Macroeconomic Forecasting Using Diffusion Indexes': the companion paper providing the theoretical framework for large-n factor-augmented forecasting.
Reference
[S6] Reference
Bernanke, Boivin, and Eliasz (2005) 'Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach': combined factor extraction with structural VAR identification.
Reference
[S7] Reference
Bai and Ng (2002) 'Determining the Number of Factors in Approximate Factor Models': proposed the IC_p1, IC_p2, IC_p3 criteria for selecting the number of factors.
Reference
Evidence and data
Check panel transformations, missing data, factor count, loading stability, and real-time forecast performance.
Calibration or measurement
Factor number, standardization, state dynamics, and release timing determine the signal extracted from the panel.
Boundaries
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