In this paper, we present a nonparametric estimation procedure for the multivariate Hawkes point process. The timeline is cut into bins and -- for each component process -- the number of points in each bin is counted. The distribution of the resulting "bin-count sequences" can be approximated by an integer-valued autoregressive model known as the (multivariate) INAR($p$) model. We represent the INAR($p$) model as a standard vector-valued linear autoregressive time series with white-noise innovations (VAR($p$)). We establish consistency and asymptotic normality for conditional least-squares estimation of the VAR($p$), respectively, the INAR($p$) model. After an appropriate scaling, these time series estimates yield estimates for the underlying multivariate Hawkes process as well as formulas for their asymptotic distribution. All results are presented in such a way that computer implementation, e.g., in R, is straightforward. Simulation studies confirm the effectiveness of our estimation procedure. Finally, we present a data example where the method is applied to bivariate event-streams in financial limit-order-book data. We fit a bivariate Hawkes model on the joint process of limit and market order arrivals. The analysis exhibits a remarkably asymmetric relation between the two component processes: incoming market orders excite the limit order flow heavily whereas the market order flow is hardly affected by incoming limit orders.