NDLI: Numerical approximation of conditional asymptotic variances using Monte Carlo simulation

Content Provider	Springer Nature Link
Author	Mak, Tak K. Nebebe, Fassil
Copyright Year	2008
Abstract	We consider in this paper the use of Monte Carlo simulation to numerically approximate the asymptotic variance of an estimator of a population parameter. When the variance of an estimator does not exist in finite samples, the variance of its limiting distribution is often used for inferences. However, in this case, the numerical approximation of asymptotic variances is less straightforward, unless their analytical derivation is mathematically tractable. The method proposed does not assume the existence of variance in finite samples. If finite sample variance does exist, it provides a more efficient approximation than the one based on the convergence of finite sample variances. Furthermore, the results obtained will be potentially useful in evaluating and comparing different estimation procedures based on their asymptotic variances for various types of distributions. The method is also applicable in surveys where the sample size required to achieve a fixed margin of error is based on the asymptotic variance of the estimator. The proposed method can be routinely applied and alleviates the complex theoretical treatment usually associated with the analytical derivation of the asymptotic variance of an estimator which is often managed on a case by case basis. This is particularly appealing in view of the advance of modern computing technology. The proposed numerical approximation is based on the variances of a certain truncated statistic for two selected sample sizes, using a Richardson extrapolation type formulation. The variances of the truncated statistic for the two sample sizes are computed based on Monte Carlo simulations, and the theory for optimizing the computing resources is also given. The accuracy of the proposed method is numerically demonstrated in a classical errors-in-variables model where analytical results are available for the purpose of comparisons.
Starting Page	333
Ending Page	344
Page Count	12
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	24
Issue Number	2
e-ISSN	16139658
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-05-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bootstrap variance estimator Existence of variances Sample size determination Statistical computing Economic Theory Probability Theory and Stochastic Processes Probability and Statistics in Computer Science Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

The influence of heteroskedastic variances on cointegration tests: A comparison using Monte Carlo simulations

Durbin’s random substitution and conditional Monte Carlo

Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities

A quadratic approximation for Jackknife estimators of the variance of sample mean functions

A moment closed form estimator for the autoregressive conditional duration model

A comparison of bootstrap and Monte-Carlo testing approaches to value-at-risk diagnosis

Numerical approximation of conditional asymptotic variances using Monte Carlo simulation

Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo

Testing parametric conditional distributions using the nonparametric smoothing method

Numerical approximation of conditional asymptotic variances using Monte Carlo simulation

Similar Documents

The influence of heteroskedastic variances on cointegration tests: A comparison using Monte Carlo simulations

Durbin’s random substitution and conditional Monte Carlo

Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities

A quadratic approximation for Jackknife estimators of the variance of sample mean functions

A moment closed form estimator for the autoregressive conditional duration model

A comparison of bootstrap and Monte-Carlo testing approaches to value-at-risk diagnosis

Numerical approximation of conditional asymptotic variances using Monte Carlo simulation

Bayesian multiple change-point estimation with annealing stochastic approximation Monte Carlo

Testing parametric conditional distributions using the nonparametric smoothing method

Numerical approximation of conditional asymptotic variances using Monte Carlo simulation