Bayesian Inference in Cointegrated VAR Models
Author | : Anders Warne |
Publisher | : |
Total Pages | : 41 |
Release | : 2006 |
Genre | : |
ISBN | : |
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Author | : Anders Warne |
Publisher | : |
Total Pages | : 41 |
Release | : 2006 |
Genre | : |
ISBN | : |
Author | : |
Publisher | : |
Total Pages | : 142 |
Release | : 2004 |
Genre | : |
ISBN | : |
Author | : Søren Johansen |
Publisher | : Oxford University Press, USA |
Total Pages | : 280 |
Release | : 1995 |
Genre | : Business & Economics |
ISBN | : 0198774508 |
This monograph is concerned with the statistical analysis of multivariate systems of non-stationary time series of type I. It applies the concepts of cointegration and common trends in the framework of the Gaussian vector autoregressive model.
Author | : Mattias Villani |
Publisher | : |
Total Pages | : 72 |
Release | : 1998 |
Genre | : |
ISBN | : 9789174741841 |
Author | : Gary Koop |
Publisher | : Now Publishers Inc |
Total Pages | : 104 |
Release | : 2010 |
Genre | : Business & Economics |
ISBN | : 160198362X |
Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.
Author | : Alessandra Canepa |
Publisher | : LAP Lambert Academic Publishing |
Total Pages | : 172 |
Release | : 2009-10 |
Genre | : |
ISBN | : 9783838314693 |
Obtaining reliable inference procedures is one of the main challenges of econometric research. Test statistics are usually based on applications of the central limit theorem. However, in order to work well the first order asymptotic approximation requires that the asymptotic distribution is an accurate approximation to the finite sample distribution. When dealing with time series models, this is not generally the case. In this book we investigate the small sample performance of various bootstrap based inference procedures when applied to vector autoregressive models. Special attention is given to Johansen s maximum likelihood method for conducting inference on cointegrated VAR models. Throughout the book, empirical applications are provided to illustrate the bootstrap method and its applications. The analysis should provide some guidance to practitioners in doubt about which inference procedure to use when dealing with cointegrated VAR models.
Author | : Gael Margaret Martin |
Publisher | : |
Total Pages | : 418 |
Release | : 1996 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Author | : Luc Bauwens |
Publisher | : OUP Oxford |
Total Pages | : 370 |
Release | : 2000-01-06 |
Genre | : Business & Economics |
ISBN | : 0191588466 |
This book contains an up-to-date coverage of the last twenty years advances in Bayesian inference in econometrics, with an emphasis on dynamic models. It shows how to treat Bayesian inference in non linear models, by integrating the useful developments of numerical integration techniques based on simulations (such as Markov Chain Monte Carlo methods), and the long available analytical results of Bayesian inference for linear regression models. It thus covers a broad range of rather recent models for economic time series, such as non linear models, autoregressive conditional heteroskedastic regressions, and cointegrated vector autoregressive models. It contains also an extensive chapter on unit root inference from the Bayesian viewpoint. Several examples illustrate the methods.
Author | : Michał Markun |
Publisher | : |
Total Pages | : 126 |
Release | : 2011 |
Genre | : Autoregression (Statistics) |
ISBN | : |
The dissertation investigates various aspects of Bayesian inference in time series econometrics. It consists of one expository chapter and two research papers. The first chapter presents on an easy example of a production function for the USA the development of Bayesian models in the context of time series analysis. The model analysed is the Cobb-Douglas production function with covariance stationary AR(1) disturbances. The methods presented are used extensively in the next two chapters. The first research paper tackles the issue of identifiation in a SVAR model with an error term being a Markov mixture of normal distributions. Non-Gaussianity can be employed for the identification of shocks. So far only classical methods have been proposed for this class of models. Bayesian methods for inference are presented, in particular an efficient method for testing homogeneity of shock process. An empirical example presents the workings of the tools developed. The topic of the second paper is the forecasting with Bayesian VARs. Owing to the shrinkage, the original Minnesota prior was reported to provide significant improvements in forecasting accuracy. Its limitations however, gave rise to research trying to relax restrictive treatment of the residual covariance matrix, and to allow for the possibility of cointegration in the system. This paper first disentangles in a unified framework and a balanced environment of optimizing choice of hyperparameters the impact on the predictive power of BVARs of developments of priors along the above two dimensions; a well known historical dataset is analyzed for this purpose. As the second contribution, the paper presents a novel prior characterized by explicit modelling of cointegration that avoids certain unattractive restrictive properties of the previously used priors; the potential of the prior for elicitation from the well established Litterman beliefs is demonstrated as well as predictive accuracy improvements over the benchmarks.
Author | : Katarina Juselius |
Publisher | : OUP Oxford |
Total Pages | : 478 |
Release | : 2006-12-07 |
Genre | : Business & Economics |
ISBN | : 0191622966 |
This valuable text provides a comprehensive introduction to VAR modelling and how it can be applied. In particular, the author focuses on the properties of the Cointegrated VAR model and its implications for macroeconomic inference when data are non-stationary. The text provides a number of insights into the links between statistical econometric modelling and economic theory and gives a thorough treatment of identification of the long-run and short-run structure as well as of the common stochastic trends and the impulse response functions, providing in each case illustrations of applicability. This book presents the main ingredients of the Copenhagen School of Time-Series Econometrics in a transparent and coherent framework. The distinguishing feature of this school is that econometric theory and applications have been developed in close cooperation. The guiding principle is that good econometric work should take econometrics, institutions, and economics seriously. The author uses a single data set throughout most of the book to guide the reader through the econometric theory while also revealing the full implications for the underlying economic model. To test ensure full understanding the book concludes with the introduction of two new data sets to combine readers understanding of econometric theory and economic models, with economic reality.