Testing for Common Autocorrelation in Data Rich Environments

This paper proposes a strategy to detect the presence of common serial correlation in high-dimensional systems. We show by simulations that univariate autocorrelation tests on the factors obtained by partial least squares outperform traditional tests based on canonical correlations.

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Year of publication:	2009-12-04
Authors:	Cubadda, Gianluca ; Hecq, Alain
Institutions:	Centro di Studi Internazionali Sull'Economia e la Sviluppo (CEIS), Facoltà di Economia
Subject:	Serial correlation common feature \| high-dimensional systems \| partial least squares

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Extent:	application/pdf
Series:	CEIS Research Paper.
Type of publication:	Book / Working Paper
Language:	English
Notes:	Number 153 11 pages
Classification:	C32 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008514824