Working Paper: CEPR ID: DP4533
Authors: Elena Angelini; Jrme Henry; Massimiliano Marcellino
Abstract: Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until the recent past. Nowadays large datasets are readily available, and models with hundreds of parameters are fastly estimated. We model these large datasets with a factor model, and develop an interpolation method that exploits the estimated factors as an efficient summary of all the available information. The method is compared with existing standard approaches from a theoretical point of view, by means of Monte Carlo simulations, and also when applied to actual macroeconomic series. The results indicate that our method is more robust to model misspecification, although traditional multivariate methods also work well while univariate approaches are systematically outperformed. When interpolated series are subsequently used in econometric analyses, biases can emerge, depending on the type of interpolation but again be reduced with multivariate approaches, including factor-based ones.
Keywords: factor model; interpolation; kalman filter; spline
JEL Codes: C32; C43; C82
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| factor-based interpolation method (C51) | lower mean squared errors (MSE) (C51) |
| factor-based interpolation method (C51) | lower mean absolute errors (MAE) (C52) |
| factor-based interpolation method (C51) | robustness to model misspecification (C50) |
| univariate methods (C29) | higher mean squared errors (MSE) (C20) |
| univariate methods (C29) | higher mean absolute errors (MAE) (C52) |
| type of interpolation employed (C51) | emergence of biases (D91) |
| multivariate approaches (C39) | mitigation of biases (D91) |