Statistical Identification of Economic Shocks by Signs in Structural Vector Autoregression

Abstract

The authors propose a new frequentist approach to sign restrictions in structural vector autoregressive models. By making efficient use of non-Gaussianity in the data, point identification is achieved which facilitates standard asymptotic inference and, hence, the assessment of theoretically implied signs and labelling of the statistically identified structural shocks. The authors illustrate the benefits of their approach in an empirical application to the US labour market.

Citation

Lanne, M. and Luoto, J. (2022), "Statistical Identification of Economic Shocks by Signs in Structural Vector Autoregression", Dolado, J.J., Gambetti, L. and Matthes, C. (Ed.) Essays in Honour of Fabio Canova (Advances in Econometrics, Vol. 44A), Emerald Publishing Limited, Leeds, pp. 165-175. https://doi.org/10.1108/S0731-90532022000044A006