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Testing for Common Trends in Nonstationary Large Datasets

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journal contribution
posted on 2023-03-27, 10:07 authored by Matteo Barigozzi, Lorenzo Trapani

We propose a testing-based procedure to determine the number of common trends in a large nonstationary dataset. Our procedure is based on a factor representation, where we determine whether there are (and how many) common factors (i) with linear trends, and (ii) with stochastic trends. Cointegration among the factors is also permitted. Our analysis is based on the fact that those largest eigenvalues of a suitably scaled covariance matrix of the data corresponding to the common factor part diverge, as the dimension N of the dataset diverges, whilst the others stay bounded. Therefore, we propose a class of randomized test statistics for the null that the pth largest eigenvalue diverges, based directly on the estimated eigenvalue. The tests only requires minimal assumptions on the data-generating process. Monte Carlo evidence shows that our procedure has very good finite sample properties, clearly dominating competing approaches when no common trends are present. We illustrate our methodology through an application to the U.S. bond yields with different maturities observed over the last 30 years.

History

Author affiliation

School of Business, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

Journal of Business & Economic Statistics

Volume

40

Issue

3

Pagination

1107 - 1122

Publisher

Informa UK Limited

issn

0735-0015

eissn

1537-2707

Copyright date

2021

Available date

2023-03-27

Language

en

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