Abstract:
This thesis is dedicated to the study of a particular class of non-linear Dynamic Factor Models, the Dynamic Factor Models with Markov Switching (MS-DFM). Combining the features of the Dynamic Factor model and the Markov Switching model, i.e. the ability to aggregate massive amounts of information and to track recurring processes, this framework has proved to be a very useful and convenient instrument in many applications, the most important of them being the analysis of business cycles.In order to monitor the health of an economy and to evaluate policy results, the knowledge of the current state of the business cycle is essential. However, it is not easy to determine since there is no commonly accepted dataset and method to identify turning points, and the official institutions announce a new turning point, in countries where such practice exists, with a structural delay of several months. The MS-DFM is able to resolve these issues by providing estimates of the current state of the economy in a timely, transparent and replicable manner.The thesis contributes to the vast literature in this area in three directions. In Chapter 3, I compare the two popular estimation techniques of the MS-DFM, the one-step and the two-step methods, and apply them to the French data to obtain the business cycle turning point chronology. In Chapter 4, on the basis of Monte Carlo simulations, I study the consistency of the estimators of the preferred technique - the two-step estimation method, and analyze their behavior in small samples. In Chapter 5, I extend the MS-DFM and suggest the Dynamical Influence MS-DFM, which allows to evaluate the evolution of the contribution of the financial sector to the dynamics of the business cycle and vice versa.
