Author: Anders WarnePublisher:
ISBN:
Category :
Languages : en
Pages : 41
Book Description
Bayesian Inference in Cointegrated VAR Models
Author: Anders WarneBayesian Analysis of Cointegrated Vector Autoregressive Models
Author: Likelihood-based Inference in Cointegrated Vector Autoregressive Models
Author: Søren JohansenPublisher: Oxford University Press, USA
ISBN: 0198774508
Category : Business & Economics
Languages : en
Pages : 280
Book Description
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.
A Bayesian Analysis of the VAR Model with a Single Cointegrating Relationship
Author: Mattias VillaniPublisher:
ISBN: 9789174741841
Category :
Languages : en
Pages : 72
Book Description
Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
Author: Gary KoopPublisher: Now Publishers Inc
ISBN: 160198362X
Category : Business & Economics
Languages : en
Pages : 104
Book Description
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.
Inference in Cointegrated Var Models
Author: Alessandra CanepaPublisher: LAP Lambert Academic Publishing
ISBN: 9783838314693
Category :
Languages : en
Pages : 172
Book Description
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.
Bayesian Inference in Models of Cointegration
Author: Gael Margaret MartinPublisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 418
Book Description
Bayesian Inference in Dynamic Econometric Models
Author: Luc BauwensPublisher: OUP Oxford
ISBN: 0191588466
Category : Business & Economics
Languages : en
Pages : 370
Book Description
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.
The Oxford Handbook of Bayesian Econometrics
Author: John GewekePublisher: Oxford University Press
ISBN: 0191618268
Category : Business & Economics
Languages : en
Pages : 576
Book Description
Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology.
Bayesian Vector Autoregressive Analysis
Author: Michał MarkunPublisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 126
Book Description
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.