Sometimes knowing the future given the present is not enough. Predicting possible futures given different defined scenarios can be more important. However, the workhorse for causality detection and impulse response, the Vector Autoregression (VAR), assumes linearity and has produced poor forecasts (Reis, 2018). Here, we introduce a vector autoencoder nonlinear autoregression neural network (VANAR) capable of both automatic time series feature extraction for its inputs and automatic functional form estimation. We evaluate VANAR in three ways: first in terms of pure forecast accuracy, second in terms of detecting the correct causality between variables, and lastly in terms of impulse response where we model trajectories given external shocks. These tests were performed on datasets with different underlying dynamics: a simulated nonlinear chaotic system, a simulated linear system, and an empirical system using Philippine macroeconomic data. Results show that VANAR significantly outperforms VAR in the forecast and causality tests. For the macroeconomic forecast, VANAR has consistently superior accuracy even over state of the art models such as SARIMA and TBATS (Hyndman et al., 2011). For the impulse response test, VANAR outperforms VAR in the linear system but both models fail to predict the shocked trajectories of the nonlinear chaotic system. VANAR was robust in its ability to model a wide variety of dynamics, from chaotic, high noise, and low data environments to complex macroeconomic systems, thus illustrating its potential usefulness in modeling more real world dynamical systems.