Reducing Dimensions in a Large TVP-VAR

45 Pages Posted: 15 Oct 2018

See all articles by Joshua C. C. Chan

Joshua C. C. Chan

University of Technology Sydney (UTS) - UTS Business School; Purdue University

Eric Eisenstat


Rodney W. Strachan

University of Queensland - School of Economics

Date Written: October 9, 2018


This paper proposes a new approach to estimating high dimensional time varying parameter structural vector autoregressive models (TVP-SVARs) by taking advantage of an empirical feature of TVP-(S)VARs. TVP-(S)VAR models are rarely used with more than 4-5 variables. However recent work has shown the advantages of modelling VARs with large numbers of variables and interest has naturally increased in modelling large dimensional TVP-VARs. A feature that has not yet been utilized is that the covariance matrix for the state equation, when estimated freely, is often near singular. We propose a specification that uses this singularity to develop a factor-like structure to estimate a TVP-SVAR for 15 variables. Using a generalization of the re-centering approach, a rank reduced state covariance matrix and judicious parameter expansions, we obtain efficient and simple computation of a high dimensional TVP-SVAR. An advantage of our approach is that we retain a formal inferential framework such that we can propose formal inference on impulse responses, variance decompositions and, important for our model, the rank of the state equation covariance matrix. We show clear empirical evidence in favour of our model and improvements in estimates of impulse responses.

Keywords: Large VAR, time varying parameter, reduced rank covariance matrix

JEL Classification: C11, C22, E31

Suggested Citation

Chan, Joshua C. C. and Eisenstat, Eric and Strachan, Rodney W., Reducing Dimensions in a Large TVP-VAR (October 9, 2018). CAMA Working Paper No. 49/2018, Available at SSRN: or

Joshua C. C. Chan

University of Technology Sydney (UTS) - UTS Business School ( email )


Purdue University

West Lafayette, IN 47907-1310
United States

Eric Eisenstat

Eisenstat ( email )

St Lucia
Brisbane, Queensland 4072

Rodney W. Strachan (Contact Author)

University of Queensland - School of Economics ( email )

Brisbane, QLD 4072

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics