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Author: Tom Boot
Publisher:
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Category :
Languages : en
Pages : 0
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Author: Tom Boot
Publisher:
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Category :
Languages : en
Pages : 0
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Author: Andreas Pick
Publisher:
ISBN:
Category :
Languages : en
Pages : 43
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Author: Jeremy Nalewaik
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Category : Economic forecasting
Languages : en
Pages : 70
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Author: James D. Hamilton
Publisher: Springer Science & Business Media
ISBN: 3642511821
Category : Business & Economics
Languages : en
Pages : 267
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Book Description
This book is a collection of state-of-the-art papers on the properties of business cycles and financial analysis. The individual contributions cover new advances in Markov-switching models with applications to business cycle research and finance. The introduction surveys the existing methods and new results of the last decade. Individual chapters study features of the U. S. and European business cycles with particular focus on the role of monetary policy, oil shocks and co movements among key variables. The short-run versus long-run consequences of an economic recession are also discussed. Another area that is featured is an extensive analysis of currency crises and the possibility of bubbles or fads in stock prices. A concluding chapter offers useful new results on testing for this kind of regime-switching behaviour. Overall, the book provides a state-of-the-art over view of new directions in methods and results for estimation and inference based on the use of Markov-switching time-series analysis. A special feature of the book is that it includes an illustration of a wide range of applications based on a common methodology. It is expected that the theme of the book will be of particular interest to the macroeconomics readers as well as econometrics professionals, scholars and graduate students. We wish to express our gratitude to the authors for their strong contributions and the reviewers for their assistance and careful attention to detail in their reports.
Author: Hans-Martin Krolzig
Publisher: Springer Science & Business Media
ISBN: 364251684X
Category : Business & Economics
Languages : en
Pages : 369
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Book Description
This book contributes to re cent developments on the statistical analysis of multiple time series in the presence of regime shifts. Markov-switching models have become popular for modelling non-linearities and regime shifts, mainly, in univariate eco nomic time series. This study is intended to provide a systematic and operational ap proach to the econometric modelling of dynamic systems subject to shifts in regime, based on the Markov-switching vector autoregressive model. The study presents a comprehensive analysis of the theoretical properties of Markov-switching vector autoregressive processes and the related statistical methods. The statistical concepts are illustrated with applications to empirical business cyde research. This monograph is a revised version of my dissertation which has been accepted by the Economics Department of the Humboldt-University of Berlin in 1996. It con sists mainly of unpublished material which has been presented during the last years at conferences and in seminars. The major parts of this study were written while I was supported by the Deutsche Forschungsgemeinschajt (DFG), Berliner Graduier tenkolleg Angewandte Mikroökonomik and Sondeiforschungsbereich 373 at the Free University and Humboldt-University of Berlin. Work was finally completed in the project The Econometrics of Macroeconomic Forecasting founded by the Economic and Social Research Council (ESRC) at the Institute of Economies and Statistics, University of Oxford. It is a pleasure to record my thanks to these institutions for their support of my research embodied in this study.
Author: Pierre Guérin
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Author: David Ardia
Publisher: Springer Science & Business Media
ISBN: 3540786570
Category : Business & Economics
Languages : en
Pages : 206
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This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.
Author: Vivek Srikrishnan
Publisher:
ISBN:
Category :
Languages : en
Pages :
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This work proposes and analyzes a Markov-switching autoregression model structurefor joint probabilistic modeling of the beam and global components of solarirradiance, which are important to simulate the performance of a variety of solarenergy conversion devices, including solar photovoltaics. The ability of this modelto assess the hourly solar resource is tested, using both a version of the modelthat is calibrated using all-year data and a version of the model that combinesindividual seasonally-calibrated models. While this simple model does not fullycapture the behavior of the solar resource, an analysis of the posterior predictivedistribution reveals strategies for improvement. A version of this model is also usedto forecast both irradiance components for a 15-minute lead time while assimilatinggeostationary satellite data into an inhomogeneous transition probability specification.The inhomogeneous specifications produce sharper predictive distributionsthan an analogous homogeneous model, but all have similar skill relative to a smartpersistence forecast.
Author: Luc Bauwens
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
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Book Description
Markov-switching models are usually specified under the assumption that all the parameters change when a regime switch occurs. Relaxing this hypothesis and being able to detect which parameters evolve over time is relevant for interpreting the changes in the dynamics of the series, for specifying models parsimoniously, and may be helpful in forecasting. We propose the class of sticky infinite hidden Markov-switching autoregressive moving average models, in which we disentangle the break dynamics of the mean and the variance parameters. In this class, the number of regimes is possibly infinite and is determined when estimating the model, thus avoiding the need to set this number by a model choice criterion. We develop a new Markov chain Monte Carlo estimation method that solves the path dependence issue due to the moving average component. Empirical results on macroeconomic series illustrate that the proposed class of models dominates the model with fixed parameters in terms of point and density forecasts.Appendix available at: 'https://ssrn.com/abstract=2965668' https://ssrn.com/abstract=2965668.
Author: Michael P. Clements
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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While there has been a great deal of interest in the modelling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time-series models. We evaluate the performance of two leading non-linear models in forecasting post-war US GNP, the self-exciting threshold autoregressive model and the Markov-switching autoregressive model. Two methods of analysis are employed: an empirical forecast accuracy comparison of the two models, and a Monte Carlo study. The latter allows us to control for factors that may otherwise undermine the performance of the non-linear models.
