Christian Macaro

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We consider spectral decompositions of multiple time series that arise in studies where the interest lies in assessing the influence of two or more factors. We write the spectral density of each time series as a sum of the spectral densities associated to the different levels of the factors. We then use Whittle's approximation to the likelihood function and(More)
Since the financial crisis of 2008, banks and bank holding companies in the United States have faced increased regulation. One of the recent changes to these regulations is known as the Comprehensive Capital Analysis and Review (CCAR). At the core of these new regulations, specifically under the DoddFrank Wall Street Reform and Consumer Protection Act and(More)
This work aims to present a full Bayesian framework to identify, extract and forecast unobserved components in time series. The major novelty is to present a probabilistic framework to analyze the identification conditions. More precisely, informative prior distributions are assigned to the spectral densities of the unobserved components. This entails a(More)
The number of rooms rented by a hotel, spending by “loyalty card” customers, automobile purchases by households—these are just a few examples of variables that can best be described as “limited” variables. When limited (censored or truncated) variables are chosen as dependent variables, certain necessary assumptions of linear regression are violated. This(More)
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