Determining the dimension of factor structures in non-stationary large datasets
14:00 - 15:30
Essex Business School, EBS.2.40
Lectures, talks and seminars
Essex Business School
Dr Mark Hallam firstname.lastname@example.org
Matteo Barigozzi from LSE will be presenting the paper titled ‘ Determining the dimension of factor structures in non-stationary large datasets’ as part of the Essex Centre for Macro and Financial Econometrics seminar series.
We propose a procedure to determine the dimension of the common factor space in a large, possibly non-stationary, dataset.
Our procedure is designed to determine whether there are (and how many) common factors (i) with linear trends, (ii) with stochastic trends, (iii) with no trends, i.e. stationary.
Our analysis is based on the fact that the largest eigenvalues of a suitably scaled co-variance 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 randomised test statistics for the null that the p-th eigenvalue diverges, based directly on the estimated eigenvalue.
The tests only requires minimal assumptions on the data, and no restrictions on the relative rates of divergence of N and T are imposed.
Monte Carlo evidence shows that our procedure has very good finite sample properties, clearly dominating competing approaches when no common factors are present.
We illustrate our methodology through an application to US bond yields with different maturities observed over the last 30 years.
A common linear trend and two common stochastic trends are found and identified as the classical level, slope and curvature factors.
This is an open event; there is no need to book. Please feel free to attend and bring your colleagues, classmates and friends.
Matteo's research mainly focuses on high-dimensional time series analysis and specifically on large dynamic factor models with extensions to the non-stationary setting, that is in presence of unit roots and co-integration or of change-points.
He is interested also in applications to macroeconomic analysis, as monetary policy making, and financial analysis, as volatility forecasting.
He is also working on: sequential testing, models for network data and spectral analysis for modelling mixed frequencies data, non-linearities, and spatial dependencies.
Before joining LSE, Matteo was post-doc researcher at the European Center for Advanced Research in Economics and Statistics (ECARES) in the Université libre de Bruxelles.