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Dimension Reduction for High‐Dimensional Vector Autoregressive Models *
Author(s) -
Cubadda Gianluca,
Hecq Alain
Publication year - 2022
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12506
Subject(s) - autoregressive model , dimension (graph theory) , vector autoregression , dynamic factor , econometrics , principal component analysis , dimensionality reduction , factor analysis , scale (ratio) , computer science , scaling , white noise , mathematics , shock (circulatory) , statistics , artificial intelligence , physics , geometry , quantum mechanics , pure mathematics , medicine
This article aims to decompose a large dimensional vector autoregressive (VAR) model into two components, the first one being generated by a small‐scale VAR and the second one being a white noise. Hence, a reduced number of common components generates the entire dynamics of the large system through a VAR structure. This modelling, which we label as the dimension‐reducible VAR, extends the common feature approach to high‐dimensional systems, and it differs from the dynamic factor model in which the idiosyncratic component can also embed a dynamic pattern. We show the conditions under which this decomposition exists. We provide statistical tools to detect its presence in the data and to estimate the parameters of the underlying small‐scale VAR model. Based on our methodology, we propose a novel approach to identify the shock that is responsible for most of the common variability at the business cycle frequencies. We evaluate the practical value of the proposed methods by simulations as well as by an empirical application to a large set of US economic variables.