Forecasting and Signal Extraction with Regularised Multivariate Direct Filter Approach
Ginters Buss ()
No 2012/06, Working Papers from Latvijas Banka
The paper studies regularised direct filter approach as a tool for high-dimensional filtering and real-time signal extraction. It is shown that the regularised filter is able to process high-dimensional data sets by controlling for effective degrees of freedom and that it is computationally fast. The paper illustrates the features of the filter by tracking the medium-to-long-run component in GDP growth for the euro area, including replication of Eurocoin-type behavior as well as producing more timely indicators. A further robustness check is performed on a less homogeneous dataset for Latvia. The resulting real-time indicators are found to track economic activity in a timely and robust manner. The regularised direct filter approach can thus be considered a promising tool for both concurrent estimation and forecasting using high-dimensional datasets and a decent alternative to the dynamic factor methodology.
Keywords: high-dimensional filtering; real-time estimation; coincident indicator; leading indicator; parameter shrinkage; business cycles; dynamic factor model (search for similar items in EconPapers)
JEL-codes: C13 C32 E32 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:ltv:wpaper:201206
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