Testing for Common Trends in Nonstationary Large Datasets

Datos Bibliográficos

ID19418015
AutoresMatteo Barigozzi (0000-0001-9090-5708, University of Bologna), Lorenzo Trapani (0000-0003-3425-2786, University of Nottingham, autor de correspondencia)
Año2022
Volumen40
Número3
Páginas1107-1122
Fecha de publicación2022-07-03
Open AccessNo
TipoARTICLE
RevistaJournal of Business and Economic Statistics (JOURNAL)
Identificadores de la revistaISSN: 0735-0015 • E-ISSN: 1537-2707
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2021.1901719
OpenAlexW3137909417
IdiomaEN
Citas recibidas4
Referencias citadas36

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

Bounded function · Cointegration · Combinatorics · Covariance matrix · Data mining · Econometrics · Eigenvalues and eigenvectors · Factor analysis · Mathematical analysis · Monte Carlo method · Null hypothesis · Sample size determination · Statistical hypothesis testing · Statistics · Computer Science · Credit Risk and Financial Regulations · Financial Risk and Volatility Modeling · Mathematics · Statistical Methods and Inference · Applied Mathematics