Latest documents http://publikationen.ub.uni-frankfurt.de/index/index/ Thu, 19 Jul 2012 10:24:02 +0200 Thu, 19 Jul 2012 10:24:02 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/25256 This chapter aims to provide a hands-on approach to New Keynesian models and their uses for macroeconomic policy analysis. It starts by reviewing the origins of the New Keynesian approach, the key model ingredients and representative models. Building blocks of current-generation dynamic stochastic general equilibrium (DSGE) models are discussed in detail. These models address the famous Lucas critique by deriving behavioral equations systematically from the optimizing and forward-looking decision-making of households and firms subject to well-defined constraints. State-of-the-art methods for solving and estimating such models are reviewed and presented in examples. The chapter goes beyond the mere presentation of the most popular benchmark model by providing a framework for model comparison along with a database that includes a wide variety of macroeconomic models. Thus, it offers a convenient approach for comparing new models to available benchmarks and for investigating whether particular policy recommendations are robust to model uncertainty. Such robustness analysis is illustrated by evaluating the performance of simple monetary policy rules across a range of recently-estimated models including some with financial market imperfections and by reviewing recent comparative findings regarding the magnitude of government spending multipliers. The chapter concludes with a discussion of important objectives for on-going and future research using the New Keynesian framework. Sebastian Schmidt; Volker Wieland workingpaper http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/25256 Thu, 19 Jul 2012 10:24:02 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22873 We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass mixture distribution and develop a semiparametric specification test explicitly tailored for such distributions. Moreover, we propose a new type of multiplicative error model (MEM) based on a zero-augmented distribution, which incorporates an autoregressive binary choice component and thus captures the (potentially different) dynamics of both zero occurrences and of strictly positive realizations. Applying the proposed model to high-frequency cumulated trading volumes of both liquid and illiquid NYSE stocks, we show that the model captures the dynamic and distributional properties of the data well and is able to correctly predict future distributions. Nikolaus Hautsch; Peter Malec; Melanie Schienle workingpaper http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/22873 Fri, 07 Oct 2011 00:00:00 +0200