An introduction to the calibration of the Schwartz (1997) reduced-form. no-arbitrage two-factor model through the expectation maximization algorithm or prediction error decomposition

This book presents an introduction to the calibration (estimation of parameters) of the Schwartz (1997) reduced-forrn, no-arbirrage two factor model by applying a combination of the Kalman filter and the maximum log-likelihood method knows as the predictive error decomposition. This book is written in such a way that a reader with primary tools in stochastic calculus and optimization (mainly the maximum log-Iikelihood method) can find the necessary tools for doing its reading without problems and understand the essential elements of the methodology. 
To pursue this purpose, in chapter 1 we will revise the model formally known as the Schwartz (1997) reduced-form, no-arbitrage two-factor model, and we will give some motivation for its calibration. In chapter 2, we will develop an introduction to the state space form and the general Kalman filter algorithm. In section 3, we will join the two previous chapters by applying the Kalman filter to the Schwartz (1997) reduced-form, no-arbitrage two-factor mode! under the approach of Schwartz (1997). Finally, in chapter 4 we show the optimization procedure to obtain the parameters as well as so me features and problems with it.