EconStor Community: CASE - Center for Applied Statistics and Economics, Humboldt University Berlin
http://hdl.handle.net/10419/127
CASE - Center for Applied Statistics and Economics, Humboldt University Berlin2016-08-25T08:04:55ZEconometrics
http://hdl.handle.net/10419/22206
Title: Econometrics
Authors: Rombouts, Jeroen V. K.; Bauwens, Luc
Abstract: Since the last decade we live in a digitalized world where many actions in human and economic life are monitored. This produces a continuous stream of new, rich and high quality data in the form of panels, repeated cross-sections and long time series . These data resources are available to many researchers at a low cost. This new erais fascinating for econometricians who can adress many open economic questions. To do so, new models are developed that call for elaborate estimation techniques. Fast personal computers play an integral part in making it possible to deal with this increased complexity.2004-01-01T00:00:00ZComputationally intensive Value at Risk calculations
http://hdl.handle.net/10419/22205
Title: Computationally intensive Value at Risk calculations
Authors: Weron, RafaĆ
Abstract: Market risks are the prospect of financial losses- or gains- due to unexpected changes in market prices and rates. Evaluating the exposure to such risks is nowadays of primary concern to risk managers in financial and non-financial institutions alike. Until late 1980s market risks were estimated through gap and duration analysis (interest rates), portfolio theory (securities), sensitivity analysis (derivatives) or "what-if" scenarios. However, all these methods either could be applied only to very specific assets or relied on subjective reasoning.2004-01-01T00:00:00ZRecursive Partitioning and Tree-based Methods
http://hdl.handle.net/10419/22203
Title: Recursive Partitioning and Tree-based Methods
Authors: Zhang, Heping
Abstract: Tree-based methods have become one of the most flexible, intuitive, and powerful data analytic tools for exploring complex data structures. The applicationsof these methods are far reaching. They include financial firms (credit cards: Altman, 2002; Frydman et al., 2002, and investments: Pace, 1995; Brennan et al., 2001), manufacturing and marketing companies (Levin et al., 1995), and pharmaceutical companies. The best documented, and arguably most popular uses of tree-based methods are in biomedical research for which classification is a central issue. For example, a clinician or health scientist may be very interested in the following question (Goldman et al., 1996, 1982; Zhang et al., 2001): Is this patient with chest pain suffering a heart attack, or does he simply have a strained muscle? To answer this question, information on this patient must be collected, and a good diagnostic test utilizing such information must be in place. Tree-based methods provide one solution for constructing the diagnostic test.2004-01-01T00:00:00ZParallel computing techniques
http://hdl.handle.net/10419/22200
Title: Parallel computing techniques
Authors: Nakano, Junji
Abstract: Parallel computing means to divide a job into several tasks and use more than one processor simultaneously to perform these tasks. Assume you have developed a new estimation method for the parameters of a complicated statistical model. After you prove the asymptotic characteristics of the method (for instance, asymptotic distribution of the estimator), you wish to perform many simulations to assure the goodness of the method for reasonable numbers of data values and for different values of parameters. You must generate simulated data, for example, 100 000 times for each length and parameter value. The total simulation work requires a huge number of random number generations and takes a long time on your PC. If you use 100 PCs in your institute to run these simulations simultaneously, you may expect that the total execution time will be 1/100. This is the simple idea of parallel computing.2004-01-01T00:00:00Z