Measuring Core Inflation By Multivariate Structural Time Series Models
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Applied Bayesian Hierarchical Methods
The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models involves complex data structures and is often described as a revolutionary development. An intermediate-level treatment of Bayesian hierarchical models and their applications, Applied Bayesian Hierarchical Methods demonstrates the advantages of a Bayesian approach
Bayesian Hierarchical Models
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website
Optimisation, Econometric and Financial Analysis
Author: Erricos Kontoghiorghes
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-05-17
Advanced computational methods are often employed for the solution of modelling and decision-making problems. This book addresses issues associated with the interface of computing, optimisation, econometrics and financial modelling. Emphasis is given to computational optimisation methods and techniques. The first part of the book addresses optimisation problems and decision modelling, with special attention to applications of supply chain and worst-case modelling as well as advances in the methodological aspects of optimisation techniques. The second part of the book is devoted to optimisation heuristics, filtering, signal extraction and various time series models. The chapters in this part cover the application of threshold accepting in econometrics, the structure of threshold autoregressive moving average models, wavelet analysis and signal extraction techniques in time series. The third and final part of the book is about the use of optimisation in portfolio selection and real option modelling.