Reducing the state space dimension in a large TVP-VAR
By Joshua C. C. Chan, Eric Eisenstat, Rodney W. Strachan. Journal of Econometrics, 2020
69 citations recorded by the source corpus.
Abstract 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 many variables. Using a generalization of the recentering 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. In a system with 15 variables, we show clear empirical evidence in favour of our model and improvements in estimates of impulse responses.
Subfields: Cognitive Neuroscience, Statistics and Probability, Transportation
View Paper: https://www.semanticscholar.org/paper/b9c62e11e3fda4f78abc536b7f36bcdd2ebfded7
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Citations in the corpus
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