[PDF] A Class Of Time Varying Parameter Structural Vars For Inference Under Exact Or Set Identification eBook

Author : Mark Bognanni
Publisher :
Page : 0 pages
File Size : 44,64 MB
Release : 2018
Category :
ISBN :

GET BOOK

This paper develops a new class of structural vector autoregressions (SVARs) with time-varying parameters, which I call a drifting SVAR (DSVAR). The DSVAR is the first structural time-varying parameter model to allow for internally consistent probabilistic inference under exact--or set--identification, nesting the widely used SVAR framework as a special case. I prove that the DSVAR implies a reduced-form representation, from which structural inference can proceed similarly to the widely used two-step approach for SVARs: beginning with estimation of a reduced form and then choosing among observationally equivalent candidate structural parameters via the imposition of identifying restrictions. In a special case, the implied reduced form is a tractable known model for which I provide the first algorithm for Bayesian estimation of all free parameters. I demonstrate the framework in the context of Baumeister and Peersman's (2013b) work on time variation in the elasticity of oil demand.

Author : Juan J. Dolado
Publisher : Emerald Group Publishing
Page : 188 pages
File Size : 33,22 MB
Release : 2022-09-21
Category : Business & Economics
ISBN : 1803828331

GET BOOK

Both parts of Volume 44 of Advances in Econometrics pay tribute to Fabio Canova for his major contributions to economics over the last four decades.

Author : Gary Koop
Publisher : Now Publishers Inc
Page : 104 pages
File Size : 47,53 MB
Release : 2010
Category : Business & Economics
ISBN : 160198362X

GET BOOK

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 : John B. Taylor
Publisher : University of Chicago Press
Page : 460 pages
File Size : 44,25 MB
Release : 2007-12-01
Category : Business & Economics
ISBN : 0226791262

GET BOOK

This timely volume presents the latest thinking on the monetary policy rules and seeks to determine just what types of rules and policy guidelines function best. A unique cooperative research effort that allowed contributors to evaluate different policy rules using their own specific approaches, this collection presents their striking findings on the potential response of interest rates to an array of variables, including alterations in the rates of inflation, unemployment, and exchange. Monetary Policy Rules illustrates that simple policy rules are more robust and more efficient than complex rules with multiple variables. A state-of-the-art appraisal of the fundamental issues facing the Federal Reserve Board and other central banks, Monetary Policy Rules is essential reading for economic analysts and policymakers alike.

Author : Lutz Kilian
Publisher : Cambridge University Press
Page : 757 pages
File Size : 12,90 MB
Release : 2017-11-23
Category : Business & Economics
ISBN : 1107196574

GET BOOK

This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.

Author : Giovanni Petris
Publisher : Springer Science & Business Media
Page : 258 pages
File Size : 49,58 MB
Release : 2009-06-12
Category : Mathematics
ISBN : 0387772383

GET BOOK

State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

Author : John Geweke
Publisher : Oxford University Press
Page : 576 pages
File Size : 32,3 MB
Release : 2011-09-29
Category : Business & Economics
ISBN : 0191618268

GET BOOK

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.

Author : Jonas Peters
Publisher : MIT Press
Page : 289 pages
File Size : 28,2 MB
Release : 2017-11-29
Category : Computers
ISBN : 0262037319

GET BOOK

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Author : Kenneth Train
Publisher : Cambridge University Press
Page : 399 pages
File Size : 36,33 MB
Release : 2009-07-06
Category : Business & Economics
ISBN : 0521766559

GET BOOK

This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.