Evaluating the performance of AIC and BIC for selecting spatial econometric models

Agiakloglou, Christos; Τσιμπάνος, Απόστολος

doi:10.1007/s43071-022-00030-x

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…In addition to the SDM, I also analysed the SDEM model specification and compared the Bayesian Information Criteria values of the two models. Based on the study by Agiakloglou and Tsimpanos (2023), this criterion also helps to select the specification that models spatial dependence appropriately when comparing SDM and SDEM. According to the BIC values, the SDM specification was found to be more appropriate, 7 confirming the theoretical consideration.…”

Section: Meanmentioning

confidence: 99%

Spatial spillover effects associated with the Sweden Democrats' growing support

Sümeghy

2024

Scand Pol Stud

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Research on the growing support of radical right parties cannot ignore the role of spatial, contextual effects and geographical proximity. One important spatial factor is the halo effect, which suggests that a radical right party is more likely to perform well in a homogeneous electoral district surrounded by ethnically diverse areas. Voter behaviour is influenced by certain neighbourhood effects, which can be spatially distant, originating from neighbouring areas. In this way, the presence of groups more inclined to support the radical right in an area can, through interaction and observations of signs or norms, modify an individual's party preference. In this paper, I model the 2018 election results of the Sweden Democrats at the level of the 6004 Swedish electoral districts using the Spatial Durbin Model specification. The spatial model shows a better fit and indicates a spillover effect of the level of education and median wealth in addition to the contextual role of ethnic diversity and its change. Changes in the proportion of non‐Europeans in neighbouring areas have had a particularly strong spillover effect, which is associated with the growing support for the Sweden Democrats.

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Section: Meanmentioning

confidence: 99%

Spatial spillover effects associated with the Sweden Democrats' growing support

Sümeghy

2024

Scand Pol Stud

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“…To take into account several parameters in the model comparison criteria, information criteria such as Akaike's criterion (aic) and Schwartz's criterion (bic) have been developed [11]. These criteria are used to select the best model among several models built on the same data set.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed model (10) allows us to quantify the quality assessment of machine learning models, and expression (11) helps to compare several models with each other. Thanks to these models, it becomes possible to automate the process of optimizing machine learning models according to the target criterion.…”

Section: Building a Multifactorial Model Of Machine Learning Model Qu...mentioning

confidence: 99%

Assessment of the quality of neural network models based on a multifactorial information criterion

Fomin,

Krykun

2024

HAIT

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The paper is devoted to the problem of assessing the quality of machine learning models in the form of neural networks in the presence of several requirements for the quality of intelligent systems. The aim of this paper is to develop a multifactorial information criterion that allows choosing a machine learning model in the form of a neural network that best meets the set of requirements for accuracy and interpretability. This goal is achieved through the development and adaptation of multifactorial information criteria for evaluating models in the form of neural networks and, in a particular case, three-layer time delay neural networks used to identify nonlinear dynamic objects. The scientific novelty of the work lies in the development of multifactorial information criteria for the quality of machine learning models that take into account the accuracy and complexity indicators, which, unlike existing information criteria, are adapted to the evaluation of models in the form of neural networks. The practical usefulness of the work lies in the possibility of automatic selection of the simplest machine learning model that provides suitable accuracy when used in intelligent systems. The practical significance of the obtained results lies in the application of the proposed criteria for selecting a machine learning model in the form of a time delay neural network for identifying nonlinear dynamic objects, which allows to increase the accuracy of modeling while ensuring the simplest architecture of the neural network.

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Unpacking the black box: Investigating the role of social protection programmes in promoting decent work and economic growth in low‐income countries

Mishra,

Grima,

Ozen

2024

Sustainable Development

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This study investigates the intricate nexus between low‐income countries, Sustainable Development Goals (SDGs), and the multifaceted phenomenon of unemployment. Amid the global endeavour to achieve SDGs, these marginalised nations assume a pivotal role, confronting distinctive challenges and opportunities. We employ meticulous analysis and structural equation modelling techniques to unveil a complex network of relationships among diverse socio‐economic indicators and unemployment across 27 low‐income countries, encompassing nations such as Afghanistan, Zambia, and others. Our empirical findings underscore the paramount importance of economic advancement, as epitomised by GDP per capita, in fostering employment prospects. Concurrently, environmental endeavours, epitomised by CO2 emissions, underscore the potential of green employment in mitigating unemployment. Investments in education and healthcare, as indicated by primary completion rates and life expectancy, emerge as pivotal determinants of employability. Moreover, access to clean energy sources and mobile cellular subscriptions indirectly influence unemployment rates, underscoring the multifaceted nature of the issue. Furthermore, our analysis reveals the intricate interplay between economic progress and social well‐being. These insights give policymakers invaluable direction, stressing the significance of tailored strategies that harness education, healthcare, technology, and environmental initiatives to alleviate unemployment, cultivate inclusive growth, and advance the overarching aspirations of sustainable development in low‐income countries.

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Evaluating the performance of AIC and BIC for selecting spatial econometric models

Cited by 7 publications

References 26 publications

Spatial spillover effects associated with the Sweden Democrats' growing support

Spatial spillover effects associated with the Sweden Democrats' growing support

Assessment of the quality of neural network models based on a multifactorial information criterion

Unpacking the black box: Investigating the role of social protection programmes in promoting decent work and economic growth in low‐income countries

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