We evaluated the advantages of the autoregressive (AR) model over the conventional Fourier transform in estimating aortic input impedance. In 10 anesthetized open-chest dogs, we digitized aortic pressure and flow at 200 Hz for 51.20 s under random ventricular pacing and subdivided them into five segments. We obtained aortic input impedance over the frequency range of 0.1-20 Hz both by AR model and by Fourier transform for various lengths of data, i.e., from one to four consecutive segments. For any given data length, the impedance spectrum estimated by the AR model was smoother than that obtained by the Fourier transform. To evaluate the accuracy of the estimated impedance, we predicted instantaneous aortic pressure of the fifth segment by convolving corresponding aortic flow with the impulse response of aortic input impedance. The prediction error was less with the AR model than that resulting from Fourier transform as long as the number of the segments was less than four. We conclude that the AR model provides a more accurate estimate of aortic input impedance than does the Fourier transform when data length is limited.