A decision support system for in-sample simultaneous equation systems forecasting using artificial neural systems

Caporaletti, Louis Eugene; Dorsey, Robert E.; Johnson, John D.; Powell, William A.

doi:10.1016/0167-9236(94)90020-5

Cited by 13 publications

(3 citation statements)

References 22 publications

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“…Results in Table 1 show that the performance of the ANN is superior to those of traditional methods (Klein, 1950;SAS, 1984) and Caporaletti et al (1994). The comparison is based on Sum of Squared Errors (SSE) in the estimation of each exogenous variable as well as Total SSE in the estimation of the whole system at different training times.…”

Section: Two-stage Ann Estimation Of the Klein Model Imentioning

confidence: 99%

“…The first attempt to capture nonlinear relationships among economic variables of a structural system with ANN was undertaken by Caporaletti et al (1994) with an in-sample estimation of Klein's Model I. Three ANNs are constructed and trained, each of which is used to forecast one of three endogenous variables of the model, i.e., consumption, investment, and private wage bills.…”

Section: First Attempt Of Forecasting Klein Model I With Annmentioning

confidence: 99%

“…The comparison is based on Sum of Squared Errors (SSE) in the estimation of each exogenous variable as well as Total SSE in the estimation of the whole system at different training times. Caporaletti et al (1994) use an ANN to estimate each endogenous variable of the system, one by one. This single equation estimation approach may not capture well the simultaneous and contemporaneous effects of other endogenous variables in the economic system as the system estimation approach used in this study.…”

Section: Two-stage Ann Estimation Of the Klein Model Imentioning

confidence: 99%

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Forecasting a Mix of Temporal and Non-Temporal Economic Variables with a Mixture-of-Experts Neural Network

Nguyen¹,

Kira²

2018

IJEF

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This study investigates a versatile forecasting technique using an integrated system of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in a mixture-of-experts architecture to solve a general economic forecasting problem involving a mix of temporal and non-temporal variables. Using Klein Model I as a context and previous estimations from traditional methods as benchmarks, the study provides evidence on the effectiveness and efficiency of this integrated system. ANN helps overcome the imposition of assumptions on the behaviors of related variables, the specification of exact relationships, and the difficulty in nonlinear estimations of the economic model. GA helps overcome the sub-optimality of the tedious trial-and-error process in network building. The flexibility of the mixture-of experts network architecture offers many alternative configurations to capture the peculiarities of variables in context before aggregating intermediate estimations into the final result. The integrated system has shown its ability in processing effectively the mixture of economic variables, and producing efficient estimations and forecasts.

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Section: Two-stage Ann Estimation Of the Klein Model Imentioning

confidence: 99%

Section: First Attempt Of Forecasting Klein Model I With Annmentioning

confidence: 99%

Section: Two-stage Ann Estimation Of the Klein Model Imentioning

confidence: 99%

See 1 more Smart Citation

Forecasting a Mix of Temporal and Non-Temporal Economic Variables with a Mixture-of-Experts Neural Network

Nguyen¹,

Kira²

2018

IJEF

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How effective are neural networks at forecasting and prediction? A review and evaluation

1998

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Despite increasing applications of artificial neural networks (NNs) to forecasting over the past decade, opinions regarding their contribution are mixed. Evaluating research in this area has been difficult, due to lack of clear criteria. We identified eleven guidelines that could be used in evaluating this literature. Using these, we examined applications of NNs to business forecasting and prediction. We located 48 studies done between 1988 and 1994. For each, we evaluated how effectively the proposed technique was compared with alternatives (effectiveness of validation) and how well the technique was implemented (effectiveness of implementation). We found that eleven of the studies were both effectively validated and implemented. Another eleven studies were effectively validated and produced positive results, even though there were some problems with respect to the quality of their NN implementations. Of these 22 studies, 18 supported the potential of NNs for forecasting and prediction. Copyright © 1998 John Wiley & Sons, Ltd.

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Neural Networks in Multicriteria Decision Support

Hanne

2013

Multicriteria Decision Aid and Artificial Intelligence

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A decision support system for in-sample simultaneous equation systems forecasting using artificial neural systems

Cited by 13 publications

References 22 publications

Forecasting a Mix of Temporal and Non-Temporal Economic Variables with a Mixture-of-Experts Neural Network

Forecasting a Mix of Temporal and Non-Temporal Economic Variables with a Mixture-of-Experts Neural Network

How effective are neural networks at forecasting and prediction? A review and evaluation

Neural Networks in Multicriteria Decision Support

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