To send 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 sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.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 sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent 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.
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.
This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.
Email your librarian or administrator to recommend adding this to your organisation's collection.