García Hiernaux, Alfredo AlejandroCasals Carro, JoséJerez Méndez, Miguel2023-06-202023-06-202005https://hdl.handle.net/20.500.14352/56624We propose two fast, stable and consistent methods to estimate time series models expressed in their equivalent state-space form. They are useful both, to obtain adequate initial conditions for a maximum-likelihood iteration, or to provide final estimates when maximum-likelihood is considered inadequate or costly. The state-space foundation of these procedures implies that they can estimate any linear fixed-coefficients model, such as ARIMA, VARMAX or structural time series models. The computational and finitesample performance of both methods is very good, as a simulation exercise shows.engtechnical reporthttps://www.ucm.es/icaeopen accessState-space modelsSubspace methodsKalman FilterSystem identificationEconometría (Economía)5302 Econometría