Identification of vector autoregressive models with nonlinear contemporaneous structure

Francesco Cordoni, Nicolas Dorémus and Alessio Moneta ()

Journal of Economic Dynamics and Control, 2024, vol. 162, issue C

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.

Keywords: Structural VAR models; Impulse response functions; Causal discovery; Nonlinearity; Additive noise models (search for similar items in EconPapers)
JEL-codes: C32 C52 E52 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Identification of Vector Autoregressive Models with Nonlinear Contemporaneous Structure (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:162:y:2024:i:c:s0165188924000447

DOI: 10.1016/j.jedc.2024.104852

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