Glocker, Christian and Kaniovski, Serguei (2020): Structural modeling and forecasting using a cluster of dynamic factor models.
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Abstract
We propose a modeling approach involving a series of small-scale dynamic factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. This approach merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent nowcasting and forecasting on a larger scale. While the CDFM has a simple structure and is easy to replicate, its forecasts are more precise than those of a wide range of competing models and those of professional forecasters. Moreover, the CDFM allows forecasters to introduce their own judgment and hence produce conditional forecasts.
Item Type: | MPRA Paper |
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Original Title: | Structural modeling and forecasting using a cluster of dynamic factor models |
Language: | English |
Keywords: | Forecasting, Dynamic factor model, Granger causality, Structural modeling |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications |
Item ID: | 101874 |
Depositing User: | Christian Glocker |
Date Deposited: | 19 Jul 2020 08:36 |
Last Modified: | 19 Jul 2020 08:36 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/101874 |