Predicting Economic Growth: Which Variables Matter

Basuchoudhary, Atin; Bang, James T.; Sen, Tinni

doi:10.1007/978-3-319-69014-8_5

Cited by 4 publications

(9 citation statements)

References 18 publications

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“…1 For instance, to predict economic recession, Liu et al (2017) compared ordinary least-squares regression results with random forest regression results and obtained a considerably higher adjusted R -squared value with random forest regression compared with ordinary least-squares regression (Nyman and Ormerod 2017). In economics, a recent book overviews various statistical-learning algorithms for predicting economic growth and recession (Basuchoudhary, Bang, and Sen 2017). In environmental science, a recent article used learning algorithms, including least absolute shrinkage and selection operator regression, random forest, and neural networks, to predict ragweed pollen concentration based on 27 years of historical data and 85 predictor variables, with the best predictive performance obtained using random forest.…”

Section: Introductionmentioning

confidence: 99%

The random forest algorithm for statistical learning

Schonlau

Zou

2020

The Stata Journal: Promoting communications on statistics and S

733

239

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Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we introduce a corresponding new command, rforest. We overview the random forest algorithm and illustrate its use with two examples: The first example is a classification problem that predicts whether a credit card holder will default on his or her debt. The second example is a regression problem that predicts the logscaled number of shares of online news articles. We conclude with a discussion that summarizes key points demonstrated in the examples.

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Section: Introductionmentioning

confidence: 99%

The random forest algorithm for statistical learning

Schonlau

Zou

2020

The Stata Journal: Promoting communications on statistics and S

733

239

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show abstract

“…We used random forest model, a machine learning approach, to identify important factors influencing intentions, and predicting decisions to become a living donor. Applications of the random forest model in the fields of economics ( 42 ), and health and environmental sciences ( 43 ) have increased rapidly in recent years. Studies that have compared results of random forest model to other approaches either found similar results ( 44 ) or that the random forest model algorithm perform well in predicting decisions compared to approaches such as ordinary least squares regression ( 45 ) and logistic regression ( 46 , 47 ).…”

Section: Article Materials and Methodsmentioning

confidence: 99%

A machine-learning approach to estimating public intentions to become a living kidney donor in England: Evidence from repeated cross-sectional survey data

Boadu

McLaughlin

Al-Haboubi

et al. 2023

Front. Public Health

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BackgroundLiving kidney organ donors offer a cost-effective alternative to deceased organ donation. They enable patients with life-threatening conditions to receive grafts that would otherwise not be available, thereby creating space for other patients waiting for organs and contributing to reducing overall waiting times for organs. There is an emerging consensus that an increase in living donation could contribute even more than deceased donation to reducing inequalities in organ donation between different population sub-groups in England. Increasing living donation is thus a priority for National Health Service Blood and Transplant (NHSBT) in the United Kingdom.MethodsUsing the random forest model, a machine learning (ML) approach, this study analyzed eight waves of repeated cross-sectional survey data collected from 2017 to 2021 (n = 14,278) as part of the organ donation attitudinal tracker survey commissioned by NHSBT in England to identify and help predict key factors that inform public intentions to become living donors.ResultsOverall, around 58.8% of the population would consider donating their kidney to a family member (50.5%), a friend (28%) or an unknown person (13.2%). The ML algorithm identified important factors that influence intentions to become a living kidney donor. They include, in reducing order of importance, support for organ donation, awareness of organ donation publicity campaigns, gender, age, occupation, religion, number of children in the household, and ethnic origin. Support for organ donation, awareness of public campaigns, and being younger were all positively associated with predicted propensity for living donation. The variable importance scores show that ethnic origin and religion were less important than the other variables in predicting living donor intention.ConclusionFactors influencing intentions to become a living donor are complex and highly individual in nature. Machine learning methods that allow for complex interactions between characteristics can be helpful in explaining these decisions. This work has identified important factors and subgroups that have higher propensity for living donation. Interventions should target both potential live donors and recipients. Research is needed to explore the extent to which these preferences are malleable to better understand what works and in which contexts to increase live organ donation.

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“…5 Last but not least, there is no need for a priori assumption or judgment on theoretical links and distribution of the variables. 6,7 This paper focuses on four main machine learning algorithms: tree regression, bagging, boosting, and random forest All of these are tree-based algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…For this purpose, if-then statements are taken into consideration, and the dataset is split according to the observed value of the input variables properly. 6 This splitting process ends when all final nodes become terminal nodes. 49 Regression trees are favorable tools for economists since they are easy to interpret and able to detect nonlinear relationships.…”

Section: Methodsmentioning

confidence: 99%

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Factors affecting per capita ecological footprint in OECD countries: Evidence from machine learning techniques

Görüş

Karagöl²

2022

Energy & Environment

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For a few decades, factors affecting environmental deterioration have been at the center of much interest This paper examines the impact of income level, disaggregated energy consumption, types of globalization level, and urbanization on per capita ecological footprint by utilizing novel machine learning techniques (tree regression, boosting, bagging, and random forest) for 27 OECD countries during 1971–2016. It is found that the random forest algorithms best fit the dataset. The empirical results exhibit that oil product consumption, electricity consumption, and gross domestic product are the most significant variables for our model. Besides, the partial dependence plots results show that economic growth and especially fossil fuel energy consumption damage the environment. These findings have important implications for both developed and developing countries for designing proper energy and environmental policies. Especially, policymakers should focus on sustainable development instead of plain economic growth.

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Predicting Economic Growth: Which Variables Matter

Cited by 4 publications

References 18 publications

The random forest algorithm for statistical learning

The random forest algorithm for statistical learning

A machine-learning approach to estimating public intentions to become a living kidney donor in England: Evidence from repeated cross-sectional survey data

Factors affecting per capita ecological footprint in OECD countries: Evidence from machine learning techniques

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