LASSO Regression and Its Application in Forecasting Macro Economic Indicators: A Study on Vietnam’s Exports

Uyen, Pham Hoang; Uyển, Võ Thị Lệ; Le, Thanh Hoa; Trung, Trinh Quoc

doi:10.1007/978-3-030-77094-5_44

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The authors argue that the LSTM-based approach outperforms models such as VAR in predicting a large number of macroeconomic variables simultaneously. Uyen et al (2022) point out that even when OLS works well, LASSO regression can still be advantageous in variable selection and prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Models for Money Demand Forecasting in the Indian Economy

Sikhwal,

Sen

2024

HSE Economic Journal

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The study investigates the predictive efficacy of various machine learning methodologies, encompassing Random Forest (RF) regression, Gradient Boosting (GB), Xtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and a deep learning technique, specifically Long Short-Term Memory (LSTM). The benchmark method employed is the autoregressive (AR) model of order 1. With a focus on forecasting money demand for the Indian economy, a crucial component for achieving the Central Bank of India's inflation targeting objective, a comprehensive monthly dataset from 1997 to 2021 is utilized.The obtained results underline the robust predictive capabilities of the employed models concerning both narrow and broad money demand forecasts. By employing a range of evaluation metrics, the study rigorously compares the predictive performance of these models. Using the expanding window cross validation with time series split, the models are cross-validated to ensure accurate forecasts

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Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Models for Money Demand Forecasting in the Indian Economy

Sikhwal,

Sen

2024

HSE Economic Journal

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“…In the realm of genomics, LASSO aids in discerning the genetic markers that are truly relevant to certain traits or diseases from the noise in high-throughput genetic data [ 15 ]. In economics, it assists in untangling the intricate relationships between numerous economic indicators, contributing to more accurate predictive models [ 16 ]. Additionally, LASSO’s utility extends to fields like climate science, where it aids in identifying the pivotal climatic variables driving specific environmental phenomena [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

LASSO Regression with Multiple Imputations for the Selection of Key Variables Affecting the Fatty Acid Profile of Nannochloropsis oculata

Andriopoulos,

Kornaros

2023

Marine Drugs

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The marine microalga Nannochloropsis oculata has garnered significant interest as a potential source of lipids, both for biofuel and nutrition, containing significant amounts of C16:0, C16:1, and C20:5, n-3 (EPA) fatty acids (FA). Growth parameters such as temperature, pH, light intensity, and nutrient availability play a crucial role in the fatty acid profile of microalgae, with N. oculata being no exception. This study aims to identify key variables for the FA profile of N. oculata grown autotrophically. To that end, the most relevant literature data were gathered and combined with our previous work as well as with novel experimental data, with 121 observations in total. The examined variables were the percentages of C14:0, C16:0, C16:1, C18:1, C18:2, and C20:5, n-3 in total FAs, their respective ratios to C16:0, and the respective content of biomass in those fatty acids in terms of ash free dry weight. Many potential predictor variables were collected, while dummy variables were introduced to account for bias in the measured variables originating from different authors as well as for other parameters. The method of multiple imputations was chosen to handle missing data, with limits based on the literature and model-based estimation, such as using the software PHREEQC and residual modelling for the estimation of pH. To eliminate unimportant predictor variables, LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis with a novel definition of optimal lambda was employed. LASSO regression identified the most relevant predictors while minimizing the risk of overfitting the model. Subsequently, stepwise linear regression with interaction terms was used to further study the effects of the selected predictors. After two rounds of regression, sparse refined models were acquired, and their coefficients were evaluated based on significance. Our analysis confirms well-known effects, such as that of temperature, and it uncovers novel unreported effects of aeration, calcium, magnesium, and manganese. Of special interest is the negative effect of aeration on polyunsaturated fatty acids (PUFAs), which is possibly related to the enzymatic kinetics of fatty acid desaturation under increased oxygen concentration. These findings contribute to the optimization of the fatty acid profile of N. oculata for different purposes, such as production of, high in PUFAs, food or feed, or production of, high in saturated and monounsaturated FA methyl esters (FAME), biofuels.

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An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance

2023

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In this work, a machine learning application was constructed to predict the logistics performance index based on economic attributes. The prediction procedure employs both linear and non-linear machine learning algorithms. The macroeconomic panel dataset is used in this investigation. Furthermore, it was combined with the microeconomic panel dataset obtained through the data envelopment analysis method for evaluating financial efficiency. The procedure was implemented in six ASEAN member countries. The non-linear algorithm of an artificial neural network performed best on the complex pattern of a collective instance of these six countries, followed by the penalized linear of the Ridge regression method. Due to the limited amount of training data for each country, the artificial neural network prediction procedure is only applicable to the datasets of Singapore, Malaysia, and the Philippines. Ridge regression fits the Indonesia, Thailand and Vietnam datasets. The results provide precise trend forecasting. Macroeconomic factors are driving up the logistics performance index in Vietnam in 2020. Malaysia logistics performance is influenced by the logistics business's financial efficiency. The results at the country level can be used to track, improve, and reform the country's short-term logistics and supply chain policies. This can bring significant gains in national logistics and supply chain capabilities, as well as support for global trade collaboration, all for the long-term development of the region.

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LASSO Regression and Its Application in Forecasting Macro Economic Indicators: A Study on Vietnam’s Exports

Cited by 3 publications

References 15 publications

Comparative Analysis of Machine Learning Models for Money Demand Forecasting in the Indian Economy

Comparative Analysis of Machine Learning Models for Money Demand Forecasting in the Indian Economy

LASSO Regression with Multiple Imputations for the Selection of Key Variables Affecting the Fatty Acid Profile of Nannochloropsis oculata

An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance

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