Suchergebnisse

Beschreibung der Verlagsausgabe: Dynamic Stochastic General Equilibrium (DSGE) models have become a standard tool in various fields of economics. This type of models has a superior theoretical foundation when compared to the Keynesian models which are traditionally used for policy analysis and forecasting. Although a lot has been done to improve the empirical properties of DSGE models, there is still a need for further research in this field. In this book, the author first considers a closed economy general equilibrium framework to empirically validate the alternative mechanisms for introducing nominal rigidities. As the comparison is done in the context of the Euro area aggregate data, the results provide guidance to researchers dealing with estimation of Euro area DSGE models in general. In the second part of the book, a coherent economic and statistical framework that approximates the structure of the EMU and explicitly accounts for the historical monetary regime change is presented. In such a framework the disaggregate information on the Euro area can be utilized, so that one can explain the area-wide aggregates, and also examine the cross-region linkages.

Introduction Identification What Is against Us? What Is for Us? Some Simple Examples of Partially Identified ModelsThe Road Ahead The Structure of Inference in Partially Identified Models Bayesian Inference The Structure of Posterior Distributions in PIMs Computational Strategies Strength of Bayesian Updating, Revisited Posterior MomentsCredible Intervals Evaluating the Worth of Inference Partial Identification versus Model Misspecification The Siren Call of Identification Comparing Bias Reflecting Uncertainty A Further Example Other Investigations of PIM versus IPMM Models Involving Misclassification Binary to Trinary Misclassification Binary Misclassification across Three Populations Models Involving Instrumental Variables What Is an Instrumental Variable? Imperfect Compliance Modeling an Approximate Instrumental Variable Further Examples Inference in the Face of a Hidden Subpopulation Ecological Inference, Revisited Further TopicsComputational Considerations Study Design Considerations Applications Concluding Thoughts What Have Others Said? What Is the Road ahead? Index.

Mit Methoden zur Umweltbewertung, die – wie die interviewgestützte Contingent Valuation Methode – auf der Messung individueller Präferenzen basieren, lässt sich der Wert einer Umweltveränderung umfassend bestimmen, aber sie erweisen sich in der Praxis oft als zu teuer. Durch die Verwendung von Bayes'schen Ansätzen können die Kosten der Kosten-Nutzen-Analyse für explizite Bewertungsstudien sowie für die implizite Bewertung durch den Nutzentransfer deutlich gesenkt werden. Darüber hinaus wird durch diesen Ansatz der Nutzentransfer erheblich zuverlässiger, so dass dieses Sparmodell der Umweltbewertung mit weitaus geringeren Einschränkungen als bislang empfohlen werden kann. Die Arbeit illustriert diese Aussagen mit einer Vielzahl simulierter und empirischer Beispiele.