Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297115 
Year of Publication: 
2024
Series/Report no.: 
LEM Working Paper Series No. 2023/07
Publisher: 
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), Pisa
Abstract: 
We propose a statistical identification procedure for recursive structural vector autoregressive (VAR) models that present a nonlinear dependence (at least) at the contemporaneous level. By applying and adapting results from the literature on causal discovery with continuous additive noise models, we show that, under certain conditions, a large class of structural VAR models is identifiable. We spell out these specific conditions and propose a scheme for the estimation of structural impulse response functions in a nonlinear setting. We assess the performance of this scheme in a simulation experiment. Finally, we apply it in a study on the effects of the macroeconomic shocks that propagate through the economy, allowing for asymmetry between responses from positive and negative impulses.
Subjects: 
Structural VAR models
Causal Discovery
Nonlinearity
Additive Noise Models
Impulse response functions
JEL: 
C32
C52
E52
Document Type: 
Working Paper

Files in This Item:
File
Size
674.01 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.