Structural breaks and outliers detection in time-series econometrics: Methods and applications

Bergamelli, Michele (2015). Structural breaks and outliers detection in time-series econometrics: Methods and applications. (Unpublished Doctoral thesis, City University London)

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This thesis contributes to the econometric literature on structural breaks analysis and outliers detection in parametric linear models. The focus is on the development of new econometric tools as well as on the analysis of novel but largely unexplored approaches. The econometric methods under analysis are illustrated using macroeconomic and financial relationships. The thesis is organised in three main chapters. In Chapter 2, we consider two novel methods to detect multiple structural breaks affecting the deterministic component of a linear system. The first is an extension of the dummy saturation method whereas the second method deals with a sequential bootstrapping procedure based on the sup-F statistic. Through an extensive Monte Carlo exercise, we explore the ability of the two approaches to detect the correct number and the correct location of the breaks. Additionally, we illustrate how to apply empirically the two procedures by investigating the stability of the Fisher relationship in the United States. In Chapter 3, we consider testing for multiple structural breaks in the vector error correction framework. First, we study the role of weak exogeneity when testing for structural breaks in the cointegrating matrix. Second, we extend the existing likelihood ratio test of Hansen (2003) to the case of unknown break dates through the specification of a minimum p-value statistic with critical values approximated by bootstrapping. Monte Carlo simulations show that the proposed statistic has good finite sample properties whilst three small empirical applications illustrate how the minimum p-value statistic can be used in practice. In Chapter 4, we tackle the purchasing power parity puzzle developing a robust estimator for the half-life of the real exchange rate. Specifically, we propose to identify outlying observations by means of a dummy saturation type algorithm designed for ARMA processes which enables to detect additional and innovative outliers as well as level shifts. An empirical application involving US dollar real exchange rates shows that the estimated half-lives are considerably shorter when outlying observations are correctly modelled, therefore shedding some light on the purchasing power parity puzzle.

Item Type: Thesis (Doctoral)
Subjects: H Social Sciences > HG Finance
Divisions: Cass Business School > Faculty of Finance

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