Skip to main content Accessibility help
×
Hostname: page-component-7c8c6479df-fqc5m Total loading time: 0 Render date: 2024-03-30T06:57:12.606Z Has data issue: false hasContentIssue false

11 - Simulation-based Bayesian inference for economic time series

Published online by Cambridge University Press:  04 August 2010

Roberto Mariano
Affiliation:
University of Pennsylvania
Til Schuermann
Affiliation:
AT&T Bell Laboratories, New Jersey
Melvyn J. Weeks
Affiliation:
University of Cambridge
Get access

Summary

Introduction

Econometric time series analysis is the discipline of using data to revise beliefs about economic questions, especially about the future. These questions have a common structure. Given data resulting from past behavior, and a set of assumptions about economic behavior (or, several sets of competing assumptions), what decision or action should be taken at the present time? The decision for action might involve public economic policy, a private economic decision, or a choice between competing assumptions.

Unfortunately economic questions are rarely laid out so explicitly. Interactions between assumptions and data are studied by a group of individuals, who (following Hildreth (1963)) we may call investigators. The investigators' tasks are complicated by the facts that data sets are constantly being updated, new models are continually being introduced and old ones modified, and the complete constellation of alternative assumptions is never neatly defined. Decisions are made by another group of individuals, who (again, following Hildreth) we may call clients. An ultimate client may be a public or private sector decision making body, in the case of policy, or the scholarly community, in the case of choices among assumptions. Investigators typically have at best a vague idea who the clients are, and exactly what use clients will wish to make of their results.

This chapter surveys some recently developed methods that hold fresh promise for investigators and their clients. These methods are based on the Bayesian paradigm for the use of economic time series, and on recent advances in simulation methods for the implementation of that paradigm.

Type
Chapter
Information
Simulation-based Inference in Econometrics
Methods and Applications
, pp. 255 - 300
Publisher: Cambridge University Press
Print publication year: 2000

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×