Resumen
This paper evaluates methods that employ Kalman Filter to estimate Diebold and Li (2006) extensions in a state-space representation, applying the Nelson and Siegel (1987) function as measure equation and different specifications for the transition equation that determines level, slope and curvature dynamics. The models that were analyzed have the following structures in transition equation: (1) AR(1) specification, employing a diagonal covariance matrix for the residuals; (2) VAR(1) specification, employing a covariance matrix calculated with Cholesky decomposition; (3) VAR(1) extension, inserting variables related to the Covered Interest Rate Parity (CIRP); (4) VAR(1) extension, including stochastic volatility components. The major findings of this paper were: (1) evaluating the latent variables dynamics, the curvature was the factor that fitted better to the stochastic volatility component; (2) in a broad sense, even though the simplest VAR(1) model was the one that provided the best out-of-sample performance in the most part of maturities and forecasting horizons, the extension inserting variables related to the CIRP was able to overcome the former specification in some of these simulations.