Machine Learning in Macro-Economic Series Forecasting

Liao, Yun

doi:10.5539/ijef.v9n12p71

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The fundamentals of the regime-lasso concepts are the dataset's clustering, model's training, and aggregation of local to global model. Economics implement K-means-clustering to cluster the data into economic states (Liao 2017), called regimes. Kijkarncharoensin and Innet (2022b) embedded this idea with the correlation distance metric to classify biomass datasets into uncorrelated regimes treated as different populations.…”

Section: Theoretical Modelsmentioning

confidence: 99%

Consistent Regime-Switching Lasso Model of the Biomass Proximate Analysis Higher Heating Value

Kijkarncharoensin

Innet

2022

Int. J. Renew. Energy Dev.

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Prediction accuracy is crucial for higher heating value (HHV) models to promote renewable biomass energy, especially its consistency is crucial when retraining data and knowledge of the range are unavailable. Current HHV models lack consistency in accuracy and interpretability due to various reasons. Thus, this study aimed to construct an interpretable and consistent proximate-based biomass HHV model on a wide-range dataset. The model, regime-lasso, integrated the concepts of regime-switching, lasso regression, and federated averaging to construct a consistent HHV model. The regime-switching partitioned the dataset into optimal regimes, and the lasso trained the regime models. The regime-lasso model is a collection of these models. It provided root mean square error of 0.4430– 0.9050, mean absolute error of 0.2743–0.6867, and average absolute error of 1.512–4.5894% in the literature’s wide-range datasets. The Kruskal–Wallis test confirmed the in-sample performance consistency at α=0.05, regardless of the training sets. In the out-of-sample situations without retraining, the model preserved its accuracy in six out of 11 datasets at α = 0.01. The interpretability of regime-lasso indicated the regime characteristic to be a factor of inconsistent prediction. The increase in FC had the maximum positive impact on HHV in the 2nd and 3rd regimes, while the increase in ASH negatively impacted the 1st and 2nd regimes. VM variation had neutral effects in all regimes. The regime-lasso solves the issues of accuracy declination and addresses the challenges in sensitivity analysis of the HHV model. The prediction accuracy issues of the model’s direct implementation were fixed.

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Section: Theoretical Modelsmentioning

confidence: 99%

Consistent Regime-Switching Lasso Model of the Biomass Proximate Analysis Higher Heating Value

Kijkarncharoensin

Innet

2022

Int. J. Renew. Energy Dev.

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“… 32. Some ML methods are similarly affected by the “curse of dimensionality.” Notably, methods like neural networks require considerable amounts of data to estimate (Bishop 2006). This is the main reason why tree-based or shrinkage methods are usually applied within the social sciences, as such methods remain applicable for very small N cases (Athey 2018; Liao 2017). …”

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confidence: 99%

Incorporating Machine Learning into Sociological Model-Building

Verhagen

2024

Sociological Methodology

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Quantitative sociologists frequently use simple linear functional forms to estimate associations among variables. However, there is little guidance on whether such simple functional forms correctly reflect the underlying data-generating process. Incorrect model specification can lead to misspecification bias, and a lack of scrutiny of functional forms fosters interference of researcher degrees of freedom in sociological work. In this article, I propose a framework that uses flexible machine learning (ML) methods to provide an indication of the fit potential in a dataset containing the exact same covariates as a researcher’s hypothesized model. When this ML-based fit potential strongly outperforms the researcher’s self-hypothesized functional form, it implies a lack of complexity in the latter. Advances in the field of explainable AI, like the increasingly popular Shapley values, can be used to generate understanding into the ML model such that the researcher’s original functional form can be improved accordingly. The proposed framework aims to use ML beyond solely predictive questions, helping sociologists exploit the potential of ML to identify intricate patterns in data to specify better-fitting, interpretable models. I illustrate the proposed framework using a simulation and real-world examples.

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“…ML methods are generally not appropriate for very small N cases, e.g. country comparisons, although they have for instance been applied to macro economic questions in past work(Liao et al, 2017;Athey, 2019).12 Lack of evaluation the fit or general appropriateness of the specifications evaluated is a key criticism of model robustness. Some have subsequently suggested to weigh models by a fit metric…”

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confidence: 99%

Incorporating Machine Learning into Sociological Model-building

Verhagen¹

2021

Preprint

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`All models are wrong, but some are useful' is an often used mantra, particularly when a model's ability to capture the full complexities of social life is questioned. However, an appropriate functional form is key to valid statistical inference, and under-estimating model complexity can lead to biased results. Unfortunately, it is unclear a-priori what the appropriate complexity of a functional form should be. I propose to use methods from machine learning to generate an estimate of the fit potential in a dataset. By comparing this fit potential with that from a functional form originally hypothesized by a researcher, a lack of model complexity in the latter can be identified. These flexible models can then be unpacked to generate understanding into the type of complexity missing. I illustrate the approach using simulations, and real-world case studies, and show how the framework is easy to implement, and leads to improved model specification.

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Machine Learning in Macro-Economic Series Forecasting

Cited by 6 publications

References 9 publications

Consistent Regime-Switching Lasso Model of the Biomass Proximate Analysis Higher Heating Value

Consistent Regime-Switching Lasso Model of the Biomass Proximate Analysis Higher Heating Value

Incorporating Machine Learning into Sociological Model-Building

Incorporating Machine Learning into Sociological Model-building

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