An evaluation of alternative methods used in the estimation of Gaussian term structure models

This paper provides an evaluation of five methods, proposed in the literature, for extracting factors used in the estimation of Gaussian affine term structure models. We assert that irrespective of the method used for extracting state variables, cross-sectional and serial correlations exist in measurement errors. However, using a simulation design which is consistent with the data, we show that parameter estimation using the Kalman filter and the model-free method are quite precise in the presence of serial and cross-sectional correlations in the error term, and, in the presence of different measurement errors, the Kalman filter demonstrates strong empirical tractability. Copyright Springer Science+Business Media New York 2015