27.12.2012.

Forecasting and Signal Extraction with Regularised Multivariate Direct Filter Approach

Working Paper 6/2012

Abstract: 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. 

JEL codes: C13, C32, E32, E37

APA: Bušs, G. (2020, 20. sep.). Forecasting and Signal Extraction with Regularised Multivariate Direct Filter Approach. Taken from https://www.macroeconomics.lv/node/2594
MLA: Bušs, Ginters. "Forecasting and Signal Extraction with Regularised Multivariate Direct Filter Approach" www.macroeconomics.lv. Tīmeklis. 20.09.2020. <https://www.macroeconomics.lv/node/2594>.
Or log in with a social profile account:

Restricted HTML

Image CAPTCHA
Up