Identification of vector autoregressive models with nonlinear contemporaneous structure - Francesco Cordoni; Nicolas Dorémus; Alessio Moneta (Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), 2024)
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: C32; C52; E52; 330; Structural VAR models; Causal Discovery; Nonlinearity; Additive Noise Models; Impulse response functions