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Author: Philip Hans Franses Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Standard unit root tests and cointegration tests are sensitive to atypical events such as outliers and structural breaks. This paper uses outlier robust estimation techniques to reduce the impact of these events on cointegration analysis. As a byproduct of computing the robust estimator, we obtain weights for all observations in the sample. These weights can be used to identify the approximate dates of the atypical events. We evaluate our method using some illustrative simulated data. Furthermore, since our robust approach involves a few additional decisions on the values of key parameters, we investigate the sensitivity of our method through extensive Monte-Carlo simulations. Finally, we present an empirical example based on real-life data to show that OLS-based cointegration tests can spuriously indicate stationarity.
Author: Philip Hans Franses Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Standard unit root tests and cointegration tests are sensitive to atypical events such as outliers and structural breaks. This paper uses outlier robust estimation techniques to reduce the impact of these events on cointegration analysis. As a byproduct of computing the robust estimator, we obtain weights for all observations in the sample. These weights can be used to identify the approximate dates of the atypical events. We evaluate our method using some illustrative simulated data. Furthermore, since our robust approach involves a few additional decisions on the values of key parameters, we investigate the sensitivity of our method through extensive Monte-Carlo simulations. Finally, we present an empirical example based on real-life data to show that OLS-based cointegration tests can spuriously indicate stationarity.
Author: Philip Hans Franses Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Unit root tests and cointegration tests are sensitive to atypical events as outliers and structural breaks. This paper uses outlier robust estimation techniques to reduce the impact of these events on cointegration analysis. As a byproduct of computing the robust estimator, we obtain weights for all observations in the sample. These weights can be used to identify approximate dates of those atypical events. We evaluate our method via some illustrative simulated data. Furthermore, since our robust approach involves a few additional decisions on the values of key parameters, we investigate the sensitivity of our method through extensive Monte- Carlo simulations. Finally, we present an empirical example based on real-life data to show that OLS based cointegration can yield spurious cointegration.
Author: Philip Hans Franses Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this paper we consider empirical econometric models for nine brands of a fast-moving nondurable consumer product using weekly observed scanning data on market share and distribution conditional on advertising, price, and promotion activities. Since the data show nonstationary characteristics, we rely on cointegration techniques to estimate long-run and short-run parameters. Additionally, as there are many outlying observations in our weekly scanning data, we apply robust cointegration methods. We find different results across robust and non-robust methods for the long-run relations between market share and distribution and for the short-run response to disequilibrium situations.
Author: Tiziano Bellini Publisher: ISBN: Category : Languages : en Pages : 28
Book Description
Cointegration analysis is particularly sensitive to outlying observations. Traditional robust approaches rely on parameter estimates based on weighting schemes to penalize aberrant units. This, in particular, is the idea underlying pseudo maximum likelihood (PML) robust estimators. Atypical observations, however, can reveal useful information about the investigated phenomenon. Aiming to detect these observations, we extend the forward search (FS) procedure to the cointegrated VAR model. The analysis is carried out building up subsets of increasing dimension and monitoring suitable statistics at each subset size. Simulation experiments and real data analysis highlight that our FS is more effective than the PML in detecting atypical units and data structures.
Author: Rudolf Dutter Publisher: Springer Science & Business Media ISBN: 364257338X Category : Mathematics Languages : en Pages : 445
Book Description
Aspects of Robust Statistics are important in many areas. Based on the International Conference on Robust Statistics 2001 (ICORS 2001) in Vorau, Austria, this volume discusses future directions of the discipline, bringing together leading scientists, experienced researchers and practitioners, as well as younger researchers. The papers cover a multitude of different aspects of Robust Statistics. For instance, the fundamental problem of data summary (weights of evidence) is considered and its robustness properties are studied. Further theoretical subjects include e.g.: robust methods for skewness, time series, longitudinal data, multivariate methods, and tests. Some papers deal with computational aspects and algorithms. Finally, the aspects of application and programming tools complete the volume.
Author: Philip Hans Franses
Author: Philip Hans Franses
Author: Philip Hans Franses
Author: Philip Hans Franses
Author: Philip Hans B. F. Franses
Author: Miguel A. Arranz
Author: G. S. Maddala
Author: Philip Hans Franses
Author: Tiziano Bellini
Author: Rudolf Dutter
Author: Heino Bohn Nielsen