Reduced Rank Regression Models in Economics and Finance

30 Pages Posted: 11 Nov 2021

See all articles by Gianluca Cubadda

Gianluca Cubadda

University of Rome Tor Vergata - Department of Economics and Finance

Alain Hecq

Maastricht University - Department of Quantitative Economics

Date Written: November 8, 2021

Abstract

This chapter surveys the importance of reduced rank regression techniques (RRR) for modelling economic and financial time series. We mainly focus on models that are capable to reproduce the presence of common dynamics among variables such as the serial correlation common feature and the multivariate autoregressive index models. Cointegration analysis, for which RRR plays a central role, is not discussed in this chapter as it deserves a specific treatment on its own. Instead, we show how to detect and model comovements in time series that are stationary or that have been stationarized after proper transformations. The motivations for the use of RRR in time series econometrics include dimension reductions which simplify complex dynamics and thus making interpretations easier, as well as pursuing efficiency gains in both estimation and prediction. Via the final equation representation, RRR also makes the nexus between multivariate time series and parsimonious marginal ARIMA models. The drawback of RRR, which is common to all the dimension reduction techniques, is that the underlying restrictions may be present or not in the data. We provide in this chapter a couple of empirical applications to illustrate concepts and methods.

Keywords: Reduced-rank regression, common features, vector autoregressive models, multivariate volatility models, dimension reduction

Suggested Citation

Cubadda, Gianluca and Hecq, Alain, Reduced Rank Regression Models in Economics and Finance (November 8, 2021). CEIS Working Paper No. 525, Available at SSRN: https://ssrn.com/abstract=3959046 or http://dx.doi.org/10.2139/ssrn.3959046

Gianluca Cubadda (Contact Author)

University of Rome Tor Vergata - Department of Economics and Finance ( email )

Via Columbia n.2
Roma, 00133
Italy

Alain Hecq

Maastricht University - Department of Quantitative Economics ( email )

P.O. Box 616
Maastricht, 6200 MD
Netherlands

HOME PAGE: http://www.maastrichtuniversity.nl/a.hecq

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