The vector error correction index model: representation, estimation and identification
Year: 2023
Type: article
Abstract: Summary This paper extends the multivariate index autoregressive model to the case of cointegrated time series of order (1,1). In this new modelling, namely the vector error-correction index model (VECIM), the first differences of series are driven by some linear combinations of the variables, namely the indexes. When the indexes are significantly fewer than the variables, the VECIM achieves a substantial dimension reduction with reference to the vector error correction model. We show that the VECIM allows one to decompose the reduced-form errors into sets of common and uncommon shocks, and that the former can be further decomposed into permanent and transitory shocks. Moreover, we offer a switching algorithm for optimal estimation of the VECIM. Finally, we document the practical value of the proposed approach by both simulations and an empirical application, where we search for the shocks that drive the aggregate fluctuations at different frequency bands in the US.
Source: Econometrics Journal
DOI: 10.1093/ectj/utad023
Authors: Gianluca Cubadda, Marco Mazzali
Institution: University of Rome Tor Vergata
Language: English
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4387906142