The Use of the Monte Carlo Method for Solving Large-Scale Problems in Neutronics

Parker, J.B.

doi:10.2307/2345038

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…The Monte-Carlo method is the nearest thing to a controlled laboratory type experiment in econometrics Intrilligator et al (1996);Johnston (1984) , Agunbiade (2007), Carlin et al, (1992), Kmenta and Joseph (1963), Parker (1972), Wagnar (1958), Olayemi and Olayide (1981) and Koutsoyiannis (2008). The MCA has been applied not only to Multicollinearity effect but also to choice of alternative estimators in determining the impact of heteroscedasticity, serial correlation and other violations of basic econometric assumptions on the performance of different estimators in a given study.…”

Section: Methodsmentioning

confidence: 99%

Estimation under Multicollinearity: A Comparative Approach Using Monte Carlo Methods

Agunbiade¹

2010

Journal of Mathematics and Statistics

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Problem statement:A comparative investigation was done experimentally for 6 different Estimation Techniques of a just-identified simultaneous three-equation econometric model with three multi-collinear exogenous variables. Approach: The aim is to explore in depth the effects of the problems of multicollinearity and examine the sensitivity of findings to increasing sample sizes and increasing number of replications using the mean and total absolute bias statistics. Results: Findings revealed that the estimates are virtually identical for three estimators: LIML, 2SLS and ILS, while the performances of the other categories are not uniformly affected by the three levels of multicollinearity considered. It was also observed that while the frequency distribution of average parameter estimates was rather symmetric under the OLS, the other estimators was either negatively or positively skewed with no clear pattern. Conclusion: The study had established that L2ILS estimators are best for estimating parameters of data plagued by the lower open interval negative level of multicollinearity while FIML and OLS respectively rank highest for estimating parameters of data characterized by closed interval and upper categories level of multicollinearity.

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Section: Methodsmentioning

confidence: 99%

Estimation under Multicollinearity: A Comparative Approach Using Monte Carlo Methods

Agunbiade¹

2010

Journal of Mathematics and Statistics

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“…In addition, an extensive Monte Carlo simulation experiment will be performed. There are many papers concerning Monte Carlo methods in the literature, including: Wagnar (), Kmenta and Joseph (), Summers (), Quandt (), Cragg (), Parker (), Hendry (), Park (), Johnston (), Capps and Grubbs (), Carlin, Polson, and Stoffer (), Koutsoyiannis (), Agunbiade (, ), Agunbiade and Iyaniwura (), Toker et al (), Özbay and Toker (, ), and Toker ().…”

Section: Introductionmentioning

confidence: 99%

The effect of target function on the predictive performance of the two‐stage ridge estimator

Toker

Özbay

2019

Journal of Forecasting

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The main thrust of this study is to consider the problem of simultaneous prediction of actual and average values of the simultaneous equations model through the target function of Shalabh (Bulletin of International Statistical Institute, 1995, 56, 1375–1390). We focus on the predictive performance of the two‐stage ridge estimator with the motivation for eliminating the disorder arising from multicollinearity. An optimal biasing parameter of the two‐stage ridge estimator is derived by a minimization process of prediction mean square error. In addition, an optimal estimator for the weight of observed value in target function is attained theoretically. The results inferred from a numerical example and a Monte Carlo experiment provide a dramatic improvement in the predictive ability of the two‐stage ridge estimator.

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Practical considerations in solving neutronics problems by the Monte Carlo method

Parker¹

1974

Annals of Nuclear Science and Engineering

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The Use of the Monte Carlo Method for Solving Large-Scale Problems in Neutronics

Abstract: This paper reviews experiences based on the use of the Monte Carlo method for solving large-scale problems in neutronics.

Cited by 4 publications

References 20 publications

Estimation under Multicollinearity: A Comparative Approach Using Monte Carlo Methods

Estimation under Multicollinearity: A Comparative Approach Using Monte Carlo Methods

The effect of target function on the predictive performance of the two‐stage ridge estimator

Practical considerations in solving neutronics problems by the Monte Carlo method

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