The ubiquity of mobile devices led to a surge in intensive longitudinal (or time series) data of individuals. This is an exciting development because personalized models both naturally tackle the issue of heterogeneities between people and increase the validity of models for applications. A popular model for time series is the Vector Autoregressive (VAR) model, in which each variable is modeled as a linear function of all variables at previous time points. A key assumption of this model is that the parameters of the true data generating model are constant (or stationary) across time. The most straightforward way to check for time-varying parameters is to fit a model that allows for time-varying parameters. In the present paper we compare two methods to estimate time-varying VAR models: the first method uses a spline-approach to allow for time-varying parameters, the second uses kernel-smoothing. We report the performance of both methods and their stationary counterparts in an extensive simulation study that reflects the situations typically encountered in practice. We compare the performance of stationary and time-varying models and discuss the theoretical characteristics of all methods in the light of the simulation results. In addition, we provide a step-by-step tutorial for both methods showing how to estimate a time-varying VAR model on an openly available individual time series dataset. How to estimate time-varying Vector Autoregressive Models? A comparison of two methods