Parametric and Nonparametric Frequentist Model Selection and Model Averaging

Ullah, Aman; Wang, Huansha

doi:10.3390/econometrics1020157

Cited by 18 publications

(5 citation statements)

References 99 publications

(106 reference statements)

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“…Frequentist Model Averaging (FMA). FMA [61,63,[244][245][246][247] is a relatively new multimodeling approach for dealing with model uncertainty that addresses several issues associated with Bayesian methods [58,183,226,230,236,242,243]. In particular, FMA doesn't require prior distributions be specified for either predictors or models and permits flexibility in weight choice for FMA estimators [246].…”

Section: Multimodel (Mm) Search and Averaging Methodsmentioning

confidence: 99%

Statistical modeling methods: challenges and strategies

Henley

Golden

Kashner

2019

Biostatistics & Epidemiology

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Statistical modeling methods are widely used in clinical science, epidemiology, and health services research to analyze data that has been collected in clinical trials as well as observational studies of existing data sources, such as claims files and electronic health records. Diagnostic and prognostic inferences from statistical models are critical to researchers advancing science, clinical practitioners making patient care decisions, and administrators and policy makers impacting the health care system to improve quality and reduce costs. The veracity of such inferences relies not only on the quality and completeness of the collected data, but also statistical model validity. A key component of establishing model validity is determining when a model is not correctly specified and therefore incapable of adequately representing the Data Generating Process (DGP). In this article, model validity is first described and methods designed for assessing model fit, specification, and selection are reviewed. Second, data transformations that improve the model's ability to represent the DGP are addressed. Third, model search and validation methods are discussed. Finally, methods for evaluating predictive and classification performance are presented. Together, these methods provide a practical framework with recommendations to guide the development and evaluation of statistical models that provide valid statistical inferences.

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Section: Multimodel (Mm) Search and Averaging Methodsmentioning

confidence: 99%

Statistical modeling methods: challenges and strategies

Henley

Golden

Kashner

2019

Biostatistics & Epidemiology

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“…3 Following Ullah and Wang (2013) and Hurvich et al (1998) we also calculated the Akaike information criterion (AIC), which again proved that Model A performs best.…”

Section: Model Comparisonmentioning

confidence: 96%

Testing moderation effects using non-parametric regressions

Assaf

Tsionas

Andrikopoulos

2020

International Journal of Hospitality Management

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Testing moderation effects is highly common in the hospitality literature. Most theories in the field depend on variables that alter the nature and direction of the relationship between two variables.While moderation continues to be heavily used, methods for testing moderation effects are not always robust. One common problem that researchers face is the need to pre-assign a particular functional form. The aim of this note is to address this problem. We describe three different nonparametric models that offer more flexibility in testing moderating effects without a need to preimpose a specific functional form. We test the three models on an interesting application involving the moderating role of corporate social responsibility (CSR) on the relationship between advertising and firm value. The results revealed interesting moderating effects that go beyond the simple linear moderation.

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“…This literature on forecast combinations (discussed here more in detail in Subsection 4.3) has become quite voluminous, see e.g. Granger (1989) and Stock and Watson (2006) for reviews, while useful surveys of FMA can be found in Burnham and Anderson (2002), Wang et al (2009), Ullah and Wang (2013) and Dormann et al (2018).…”

Section: Frequentist Model Averagingmentioning

confidence: 99%

Model Averaging and its Use in Economics

Steel¹

2017

Preprint

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The method of model averaging has become an important tool to deal with model uncertainty, for example in situations where a large amount of different theories exist, as are common in economics. Model averaging is a natural and formal response to model uncertainty in a Bayesian framework, and most of the paper deals with Bayesian model averaging. The important role of the prior assumptions in these Bayesian procedures is highlighted. In addition, frequentist model averaging methods are also discussed. Numerical methods to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on uncertainty regarding the choice of covariates in normal linear regression models, but the paper also covers other, more challenging, settings, with particular emphasis on sampling models commonly used in economics. Applications of model averaging in economics are reviewed and discussed in a wide range of areas, among which growth economics, production modelling, finance and forecasting macroeconomic quantities.

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Parametric and Nonparametric Frequentist Model Selection and Model Averaging

Cited by 18 publications

References 99 publications

Statistical modeling methods: challenges and strategies

Statistical modeling methods: challenges and strategies

Testing moderation effects using non-parametric regressions

Model Averaging and its Use in Economics

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