This paper investigates the nonlinearity in the effects of news shocks about technological innovations. In a maximally flexible logistic smooth transition vector autoregressive model, state-dependent effects of news shocks are identified based on medium-run restrictions. We propose a novel approach to impose these restrictions in a nonlinear model using the generalized forecast error variance decomposition. We compute generalized impulse response functions that allow for regime transition and find evidence of state-dependency. The results also indicate that the probability of a regime switch is highly influenced by the news shocks.
smooth transition vector autoregressive model nonlinear time-series model news shock generalized impulse responses generalized forecast error variance decomposition