Skip to main content Accessibility help
×
Home
Hostname: page-component-59b7f5684b-569ts Total loading time: 0.342 Render date: 2022-09-27T05:53:41.946Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

Introduction

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

The simulation-based inference literature grew out of problems faced by microeconometricians in estimating discrete choice models in cross-sections. The classic problem is the multinomial probit model which encounters the computational barrier if there are more than four alternatives. Generically the problem is one of evaluating highly complex conditional expectations no matter what the data structure. Not surprisingly, time series econometricians have recently made increased use of the simulation-based techniques to solve some computational issues of their own. Some themes which emerge are the modeling of highly non-linear financial instruments, conditioning on pre-sample information and the evaluation of posterior distributions in a Bayesian context. This section presents several examples from this rapidly growing literature.

Many of the applications are in the area of empirical finance and macro-econometrics. The first chapter in this section by Christensen and Kiefer is a poignant example. Their contribution is a great step forward toward bridging the gap between recent developments in theoretical finance and econometric modeling. The finance theoretical point of departure is a probability measure under which suitably discounted security price processes are martingales. Knowledge of this equivalent martingale measure allows one to simulate long realizations of the theoretical price process whose moments can be matched to the empirical moments. Specifically, when evaluating option prices empirically, the analyst is forced to make the false assumption of complete markets with no arbitrage in order to make the problem tractable.

Type
Chapter
Information
Simulation-based Inference in Econometrics
Methods and Applications
, pp. 179 - 182
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.

  • Introduction
  • Edited by Roberto Mariano, University of Pennsylvania, Til Schuermann, AT&T Bell Laboratories, New Jersey, Melvyn J. Weeks, University of Cambridge
  • Book: Simulation-based Inference in Econometrics
  • Online publication: 04 August 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511751981.010
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.

  • Introduction
  • Edited by Roberto Mariano, University of Pennsylvania, Til Schuermann, AT&T Bell Laboratories, New Jersey, Melvyn J. Weeks, University of Cambridge
  • Book: Simulation-based Inference in Econometrics
  • Online publication: 04 August 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511751981.010
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.

  • Introduction
  • Edited by Roberto Mariano, University of Pennsylvania, Til Schuermann, AT&T Bell Laboratories, New Jersey, Melvyn J. Weeks, University of Cambridge
  • Book: Simulation-based Inference in Econometrics
  • Online publication: 04 August 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511751981.010
Available formats
×