Endogenous Moderator Models: What They are, What They Aren’t, and Why it Matters

2022 ◽  
pp. 109442812110651
Author(s):  
Jose M. Cortina ◽  
Christian Dormann ◽  
Hannah M. Markell ◽  
Sheila K. Keener
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Second Stage ◽  
Superficial Resemblance ◽  
Path Analytic ◽  
The Relationship

Models that combine moderation and mediation are increasingly common. One such model is that in which one variable causes another variable that, in turn, moderates the relationship between two other variables. There are many recent examples of these Endogenous Moderator Models (EMMs). They bear little superficial resemblance to second-stage moderation models, and they are almost never conceptualized and tested as such. We use path analytic equations to show that this is precisely what EMMs are. Specifically, we use these path analytic equations and a review of recent EMMs in order to show that these models are seldom conceptualized or tested properly and to understand the best ways to handle such models. We then use Monte Carlo simulation to show the consequences of testing these models as they are typically tested rather than as second-stage moderation models. We end with recommendations and provide example datasets and code for SPSS and R.

Exit Condition for Probabilistic Assessment Using Monte Carlo Method

2010 ◽  
Vol 10 (1) ◽  
pp. 1-9
Author(s):  
Jakub Valihrach ◽  
Petr Konečný
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optimization Design ◽  
Random Variable ◽  
Probability Of Failure ◽  
Probabilistic Assessment ◽  
Theoretical Derivation ◽  
The Monte Carlo Method ◽  
The Relationship

Exit Condition for Probabilistic Assessment Using Monte Carlo Method This paper introduces a condition used to exit a probabilistic assessment using the Monte Carlo simulation, and to evaluate it with regard to the relationship between the computed estimate of the probability of failure and the target design probability. The estimation of probability of failure is treated as a random variable, considering its variance that is dependent on the number of performed Monte Carlo simulation steps. After theoretical derivation of the decision condition, it is tested numerically with regard to its accuracy and computational efficiency. The condition is suitable for optimization design using the Monte Carlo method.

scholarly journals Evaluating Profitability of Individual Timber Deliveries in the US South

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 437
Author(s):  
Joseph L. Conrad
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gps Tracking ◽  
Hourly Wage ◽  
Us South ◽  
Wood Supply ◽  
The Us ◽  
Dependent Variables ◽  
The Relationship ◽  
Harvest Site

Timber transportation is an essential and often unprofitable segment of the wood supply chain. This study evaluated the profitability of individual timber deliveries for log truck owners in the US South. Origin and destination data were collected from 909 deliveries from 257 harvest sites. Travel time and distance were estimated using ArcGIS and GPS tracking. Monte Carlo Simulation was used to calculate 1000 unique combinations of payload, harvest site turn-time, mill turn-time, and percent-loaded km, yielding a dataset of 909,000 deliveries. Hauling costs and revenues for each delivery were estimated using published estimates. Driver wages were estimated in two ways: an hourly wage of $30.60 (USD) and 30% of the gross revenue from the load being delivered. Logistic regression was used to evaluate the relationship between six dependent variables and profitability. Only 14% of deliveries were profitable when the driver was paid an hourly wage versus 42% when the driver was paid 30% of gross revenue. Deliveries with one-way haul distances between 49 and 113 km (31–70 mi) were least likely to be profitable. Many deliveries could be profitable if logging businesses and mills reduced turn-times to under 20 min at mills and 30 min at harvest sites.

The Structure of Rejection: Implications of Sociometric Theory for Larger Groups

1994 ◽  
Vol 79 (2) ◽  
pp. 835-842 ◽  
Author(s):  
Donal E. Muir
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Group Analysis ◽  
The Other ◽  
Group Context ◽  
The Relationship

Established principles governing acceptance and rejection in dyads and triads are extended to larger groups by analyzing them as made up of such elements. In this larger-group context, a change in any dyad has direct implications for all triads involving that dyad and indirect implications for all triads containing any of the other dyads of those triads. Consequently, a change in the relationship between two individuals can produce effects cascading through the larger group. Analysis based on Monte Carlo simulation indicates that larger groups are more likely to attain sociometric stability when a preponderance of members are rejected, unless structured to encourage mutual acceptance.

CHARACTERISTIC AGE AND TRUE AGE OF PULSARS

2013 ◽  
Vol 23 ◽  
pp. 95-98
Author(s):  
LONG JIANG ◽  
CHENG-MIN ZHANG ◽  
ALI TANNI ◽  
HAI-HUI ZHAO
Keyword(s):  
Magnetic Field ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Millisecond Pulsar ◽  
Spin Period ◽  
Field Evolution ◽  
Period Derivative ◽  
The Difference ◽  
The Relationship ◽  
The Magnetic Field

Age of a pulsar is a useful parameter, but it is difficult to get the age from observation. We can only derive the characteristic age from the observed parameters: spin period (P) and period derivative (Ṗ). In this paper, we discussed the relationship between characteristic age and magnetic field of a pulsar. Monte Carlo simulation is also used to support the idea: it is useless to study the magnetic field evolution using characteristic age. From some observation evidences we get that: the characteristic age cannot be used as true age, especially for millisecond pulsar (MSP). The difference between them is also discussed. From the studying of breaking index and MSP's initial spin period (P0), we get the conclusion that: the problem cannot be resolved using different radiation models.

An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings

2017 ◽  
Vol 12 (3) ◽  
pp. 252-266 ◽  
Author(s):  
Don Cyr ◽  
Lester Kwong ◽  
Ling Sun
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Tail Dependence ◽  
Copula Function ◽  
Bivariate Distribution ◽  
Nonlinear Relationship ◽  
Futures Prices ◽  
The Relationship ◽  
Jel Classifications

AbstractThis paper explores the nonlinearities of the bivariate distribution of Bordeaux en primeur, or wine futures, prices and Parker “barrel ratings” for the period of 2004 through 2010. In particular, copula-function methodology is introduced and employed to examine the nature of the bivariate distribution. Our results show a significant nonlinear relationship between Parker ratings and wine prices, characterized by significant positive tail dependence and higher correlation between high ratings and high prices. Marginal distributions for Parker ratings and wine prices are then identified and Monte Carlo simulation is employed to operationalize the relationship for risk-management purposes. (JEL Classifications: C19, G13, L66)

scholarly journals Application of Duration Measure in Quantifying the Sensitivity of Project Returns to Changes in Discount Rates

2019 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Vahidreza Yousefi ◽  
Siamak Haji Yakhchali ◽  
Jolanta Tamošaitienė
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Project Management ◽  
Discount Rate ◽  
Construction Projects ◽  
Project Manager ◽  
Management Systems ◽  
Duration Measure ◽  
The Relationship

In this research, the concept of Duration with a new application in project management has been defined. The Duration of each project provides the project manager with a combined measure containing concepts of return, cost and time of the project. Further in this article, the changes in project return, based on different assumptions such as discount rate, have been examined. To examine the effect of the changes in these factors, the Monte Carlo simulation has been used. The relationship between these factors is nonlinear which reflects the great importance of investment on appropriate risk management systems. The data from a set of construction projects have been used in order to verify the results of this study. Similar relationships can be expected to exist in other industries as well.

Measures of family size preferences

1986 ◽  
Vol 18 (3) ◽  
pp. 357-370
Author(s):  
J. C. Barrett
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Breast Feeding ◽  
Family Size ◽  
England And Wales ◽  
Contraceptive Effectiveness ◽  
The Relationship ◽  
Monte Carlo Simulation Model

SummaryEstimates of preferred family sizes are derived from those achieved at various durations of marriage, using a Monte Carlo simulation model of reproductive histories, slightly modified in its representation of breast-feeding. The method of estimation consists in inverting the relationship between these two distributions (wanted and achieved births) which form respectively an input to and an output of the model. The increasing preference for families of two children is shown in cohorts married in England and Wales in 1951, 1956, 1961, 1966 and 1971 for women's ages at marriage 20–24 and 25–29. Sensitivity to assumptions regarding contraceptive effectiveness is tested, and ranges established for estimates. The evolution and stability of family size preferences are discussed.

scholarly journals Using Risk Simulation to Reduce the Capital Cost Requirement for a Programme of Capital Projects

2017 ◽  
Vol 15 (1) ◽  
pp. 1-13
Author(s):  
Francois Joubert ◽  
Leon Pretorius
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cost Estimation ◽  
Research Result ◽  
Simulation Software ◽  
Capital Projects ◽  
Risk Simulation ◽  
The Individual ◽  
The Relationship

This paper combines various concepts related to (i) project risk management, (ii) Monte Carlo simulation, (iii) project contingency cost estimation, and (iv) the relationship between project and programme risks, to illustrate that the contingency requirements are lower when simulating all the risks in the programme when comparing it with the individual project contingency requirement. A case study organisation provided 86 quantified risk registers related to port and rail capital projects. For each of these risk registers, the project contingency was estimated using a prescribed risk register template and Monte Carlo simulation software. The same 86 quantified risk registers were then used to simulate the programme contingency. The simulation results indicated that the programme contingency requirement was approximately 8% points lower than that of the sum of the individual projects. The first implication of this research result is that, should borrowed capital be used to fund the projects, the interest bill would be higher when calculating project contingency on a project-by-project basis. The second is that regularly appearing low probability, high impact risks, should be identified and these risks should be quantified not in the projects themselves, but in a centrally managed, programme cost contingency fund.

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