Forecasting with growth curves: An empirical comparison

Meade, Nigel; Islam, Tarikul

doi:10.1016/0169-2070(94)00556-r

Cited by 129 publications

(68 citation statements)

References 17 publications

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“…The IPT function is quite flexible as regards the point of inflexion, which can be anywhere between its upper and lower bounds. Moreover, the IPT function offers wide variations in the degree of symmetry for a given point of inflexion (Meade andIslam 1995, Bewley andFiebig 1988).^ Because it is the certainty equivalents and not the utility levels that are measured with error, the inverse functions are estimated (Smidts 1997;see Appendix).…”

Section: Fitting a Continuous Functionmentioning

confidence: 99%

The Shape of Utility Functions and Organizational Behavior

2003

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B ased on measurements among 332 owner-managers, we investigate how the global shape of the utility function (i.e., S-shaped versus concave or convex over the total range of outcomes) relates to choice behavior. We find that the global shape of the utility function differs across decision makers (about one-third of the owner-managers exhibit an S-shaped utility function) and that the global shape is linked to organizational behavior (i.e., the production system employed), a result that does not change when using different methods to identify the decision maker's global shape of the utility function. The decision maker's risk attitude (risk averse or risk seeking) does not affect the choice of the production system. Whereas the degree of risk aversion, based on the local shape of the utility function, may be important in explaining owner-managers' trading behavior (Pennings and Smidts 2000), more structural organizational behavior appears to be linked to the global shape of the utility fimction.

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Section: Fitting a Continuous Functionmentioning

confidence: 99%

The Shape of Utility Functions and Organizational Behavior

2003

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“…Nevertheless, third-party research studies that compare many models often find virtue in simplicity. For example, Meade and Islam (1995) compare 17 forecasting models using 25 time series in 15 countries for the development of telecommunications markets and find that simple logistic and the Gompertz models significantly outperform more complex models. Dror and Steinberg (2006) find that simple clustering for constructing experimental designs for multivariate generalized linear models outperforms more complex and sophisticated methods.…”

Section: Supposedly Realistic Assumptions Is Also a Bogus Benefitmentioning

confidence: 99%

Editorial—It’s the Findings, Stupid, Not the Assumptions

Shugan

2007

Marketing Science

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Observing reality is especially valuable. However, without models, every situation at every time on every variable would be unpredictable. Assumptions allow models and theories to assert constancy. Assumptions distill and simplify reality by dismissing the conspicuous but irrelevant. Criticizing assumptions as unrealistic is absurd. Abstraction is the precise virtue of an assumption. For example, seldom are we prisoners facing interrogation, yet the prisoner’s dilemma remains relevant. The adage “A bird in the hand is worth two in the bush” is relevant for more than birds. Unrealistic assumptions that deny current beliefs breed great new theories. Assumptions are analogous to the basic ingredients in a gourmet recipe. Only the final product of the recipe dictates whether the ingredients suffice. Similarly, assumptions are realistic when they produce good theories, satisfactory predictions, valuable implications, and correct recommendations. Output matters far more than input. Realism is only an issue when creatively diagnosing poorly performing models, not when judging model performance. Assumptions are the source of value in empirical analyses. If data sets were truly the source of value, empirical research studies would only greatly devalue the raw data by dramatically reducing rich observations to a few meager summary statistics or estimated parameters. Most empirical research makes a contribution by ignoring (assuming away) most information in the data. We must dramatically shift our attention far away from the hopeless pursuit and sophistry of realistic assumptions to the contribution those assumptions produce. There are scientific methods for evaluating model output (i.e., predictions, findings, implications, recommendation) on criteria such as accuracy, reliability, validity, robustness, and so on. No corresponding objective scientific methods exist for evaluating realism. Realism depends only on personal taste.

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“…Another advantage of our formulation of the model is that its parameters have clear, physical interpretations. In addition, recent studies have shown that the simple logistic often outperforms more complicated parameterizations, which have the disadvantage of losing clear physical interpretations for their parameters (50]. (often 1 year) to the average number of women of childbearing age (usually taken as age in the population during the period.…”

Section: A No(t)mentioning

confidence: 99%