This work investigates the time series properties of the unemployment rate of the Spanish regions over the period 1976-2011. For that purpose, the authors employ the PANIC procedures of Bai and Ng (2004), which allows to decompose the observed unemployment rate series into common factor and idiosyncratic components. This enables the authors to identify the exact source behind the hysteretic behaviour found in Spanish regional unemployment. Overall, the analysis with three different proxies for the excess of labour supply renders strong support for the hysteresis hypothesis, which appears to be caused by a common stochastic trend driving all the regional unemployment series. In the second part of the analysis the authors try to determine the macroeconomic and institutional factors that are able to explain the time series evolution of the common factor, and in turn help us shed light on the ultimate sources of hysteresis. The reader shall see how the variables that the empirical analysis emphasises as relevant closely fit into the main causes of the Spanish unemployment behaviour. Finally, some policy considerations drawn from the results are presented.
Algebraic Identification and Estimation Methods in Feedback Control Systems presents a model-based algebraic approach to online parameter and state estimation in uncertain dynamic feedback control systems. This approach evades the mathematical intricacies of the traditional stochastic approach, proposing a direct model-based scheme with several easy-to-implement computational advantages. The approach can be used with continuous and discrete, linear and nonlinear, mono-variable and multi-variable systems. The estimators based on this approach are not of asymptotic nature, and do not require any statistical knowledge of the corrupting noises to achieve good performance in a noisy environment. These estimators are fast, robust to structured perturbations, and easy to combine with classical or sophisticated control laws. This book uses module theory, differential algebra, and operational calculus in an easy-to-understand manner and also details how to apply these in the context of feedback control systems. A wide variety of examples, including mechanical systems, power converters, electric motors, and chaotic systems, are also included to illustrate the algebraic methodology. Key features: Presents a radically new approach to online parameter and state estimation. Enables the reader to master the use and understand the consequences of the highly theoretical differential algebraic viewpoint in control systems theory. Includes examples in a variety of physical applications with experimental results. Covers the latest developments and applications. Algebraic Identification and Estimation Methods in Feedback Control Systems is a comprehensive reference for researchers and practitioners working in the area of automatic control, and is also a useful source of information for graduate and undergraduate students.