Template-Type: ReDIF-Paper 1.0
Author-Name: Ginters Buss
Author-X-Name-First: Ginters
Author-X-Name-Last: Buss
Author-Workplace-Name: Bank of Latvia
Title: Forecasting and Signal Extraction with Regularised Multivariate Direct Filter Approach
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.
Creation-Date: 2012-12-27
File-URL: https://www.bank.lv/images/stories/pielikumi/publikacijas/petijumi/WP_6_2012_buss-final-1.pdf
File-Format: Application/pdf
File-URL: https://www.macroeconomics.lv/sites/default/files/wp_6_2012.pdf
File-Format: Application/pdf
Number: 2012/06
Classification-JEL: C13, C32, E32, E37
Keywords: high-dimensional filtering, real-time estimation, coincident indicator, leading indicator, parameter shrinkage, business cycles, dynamic factor model
X-File-Ref: https://repec.bank.lv/refs/wpaper201206.txt
Handle: RePEc:ltv:wpaper:201206