The area of controller performance monitoring, assessment, and diagnosis for model predictive control (MPC) has seen an increase in interest in recent years. A frequently identified cause of degraded performance is mismatch between the plant model used in the controller and the true dynamics of the plant. Most recent research focuses on locating plant model mismatch in order to reduce the considerable effort required to re-identify the model. In this paper, we present a novel autocovariance-based plant model mismatch estimation approach for unconstrained MPC based on linear state space models. We show that (additive) plant model mismatch can be quantified as the solution of an optimization problem which minimizes the discrepancy between the sample autocovariance of plant outputs and the corresponding value obtained from theoretical predictions. We illustrate our theoretical results with two simulation case studies, demonstrating good performance in estimating parametric mismatch.