<?xml version="1.0" encoding="UTF-8"?>
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<title>Non-traditional Outputs</title>
<link href="http://hdl.handle.net/10453/11549" rel="alternate"/>
<subtitle/>
<id>http://hdl.handle.net/10453/11549</id>
<updated>2013-05-19T22:24:49Z</updated>
<dc:date>2013-05-19T22:24:49Z</dc:date>
<entry>
<title>Econometrics</title>
<link href="http://hdl.handle.net/10453/17610" rel="alternate"/>
<author>
<name>Geweke John</name>
</author>
<author>
<name>Horowitz Joel</name>
</author>
<author>
<name>Pesaran Hashem</name>
</author>
<id>http://hdl.handle.net/10453/17610</id>
<updated>2012-03-12T11:24:33Z</updated>
<published>2008-01-01T00:00:00Z</published>
<summary type="text">Econometrics
Geweke John; Horowitz Joel; Pesaran Hashem
Durlauf, SN; Blume, LE
As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly. Major advances have taken place in the analysis of cross-sectional data by means of semiparametric and nonparametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledged and attempts have been made to take it into account either by integrating out its effects or by modelling the sources of heterogeneity when suitable panel data exist. The counterfactual considerations that underlie policy analysis and treatment valuation have been given a more satisfactory foundation. New time-series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Nonlinear econometric techniques are used increasingly in the analysis of cross-section and time-series observations. Applications of Bayesian techniques to econometric problems have been promoted largely by advances in computer power and computational techniques. The use of Bayesian techniques has in turn provided the investigators with a unifying framework where the tasks of forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process, thus providing a basis for â¿¿real time econometricsâ¿¿.
</summary>
<dc:date>2008-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Stochastic differential equations with jumps: Simulation</title>
<link href="http://hdl.handle.net/10453/16828" rel="alternate"/>
<author>
<name>Bruti Liberati Nicola</name>
</author>
<author>
<name>Platen Eckhard</name>
</author>
<id>http://hdl.handle.net/10453/16828</id>
<updated>2012-02-02T11:14:04Z</updated>
<published>2010-01-01T00:00:00Z</published>
<summary type="text">Stochastic differential equations with jumps: Simulation
Bruti Liberati Nicola; Platen Eckhard
Rama Cont et al
NA
</summary>
<dc:date>2010-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Duffie-Singleton Model</title>
<link href="http://hdl.handle.net/10453/16827" rel="alternate"/>
<author>
<name>Schlogl L</name>
</author>
<author>
<name>Schlogl Erik</name>
</author>
<id>http://hdl.handle.net/10453/16827</id>
<updated>2012-02-02T11:14:04Z</updated>
<published>2010-01-01T00:00:00Z</published>
<summary type="text">Duffie-Singleton Model
Schlogl L; Schlogl Erik
Rama Cont et al
NA
</summary>
<dc:date>2010-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Monte Carlo simulations</title>
<link href="http://hdl.handle.net/10453/16826" rel="alternate"/>
<author>
<name>Jackel Peter</name>
</author>
<author>
<name>Platen Eckhard</name>
</author>
<id>http://hdl.handle.net/10453/16826</id>
<updated>2012-02-02T11:14:03Z</updated>
<published>2010-01-01T00:00:00Z</published>
<summary type="text">Monte Carlo simulations
Jackel Peter; Platen Eckhard
Rama Cont et al
NA
</summary>
<dc:date>2010-01-01T00:00:00Z</dc:date>
</entry>
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