In this paper, a novel method based on analysis of dynamic response of vestibular system for diagnosis of Parkinson's Disease (PD) is introduced. Electrovestibulography (EVestG) signals are recorded from the ear canal in response to a vestibular stimulus. EVestG signals are in fact the vestibular response modulated by more cortical brain signals. We used EVestG data of 20 patients with PD and 26 age-matched healthy controls recorded in a previous study. We calculated the Katz Fractal Dimension (FD) of the extracted timing signal of firings during contralateral and ipsilateral stimuli of both left and right ear. We used multivariate analysis of variance (MANOVA) to select pairs of features showing the most significant differences between the groups. Then, Linear and Quadratic Discriminant (LDA, QDA) classification algorithms were applied on the selected features. The results have shown above 77.27% accuracy. Given the small population of the subjects and the patients were at different stage of disease, the results encourage continuing exploration of the application of EVestG for PD diagnosis and perhaps as a quick and non-invasive screening tool.