title: Implementation of Machine Learning Methods in Applied Economics
Creators: name: Paes Herrera, Gabriel
Publisher: name: Griffith University
Year: 2022
Subjects: subject: Machine Learning Methods
subject: Applied Economics
subject: FOS: Economics and business
subject: energy commodities

description: This thesis aims to expand the research frontier and contribute to the literature on the application of machine learning (ML) in economics. Traditional econometric methods and machine learning modes have their advantages and disadvantages. Analysis techniques are constantly evolving to provide better solutions to solve problems and extract insights from data. This work includes four chapters, each of which explores a relevant application of ML algorithms in different economic segments. Apart from that, the thesis also includes an introduction and a final chapter containing concluding remarks. The first study explores the application of ML methods for long-term forecasting of energy commodities. Time series of energy commodities prices are known to contain complex nonlinear relationships, seasonality, and structural breaks. Machine learning models are flexible data-driven methods capable of modelling nonlinearities and uncovering complex structures. Therefore, the artificial neural network (ANN) and random forests ML models are employed, in addition to econometric benchmark approaches. The analyses focus on longterm forecast performance, as this is key information for stakeholders to negotiate futures contracts in the international energy market. Further, the study includes the three main energy sources consumed around the world from six trade spots. Results present evidence that ML models outperform traditional econometric methods and are a great alternative for long-term forecasting analysis in this sector. Additionally, the data-driven approaches are also better at predicting turning points. The findings of this work are important to provide more accurate forecasts and support policymakers and energy market stakeholders. The second study examines the relationship between the accuracy of forecasting methods and time series characteristics. Time series data may contain different features, such as seasonality and unit root, that are processed more efficiently by a specific forecasting method. Researchers have numerous forecasting techniques available and choosing one can be challenging. Previous studies have explored the correlation between traditional models and time series attributes and have been guiding practitioners for years. However, studies addressing this issue do not include novel ML techniques. Therefore, this work performs a comprehensive analysis employing state-of-the-art machine learning and deep learning methods, while accounting for several time series aspects. This chapter explores the tourism market, an important economic sector that is responsible for about 10% of the world's GDP, The study analyses Australian inbound tourism time series data from 14 countries, including developed and developing economies, across five continents. Results reveal that the Long Short-Term Memory (LSTM) ANN deep learning model performs best in most cases. However, a strong presence of unit roots and structural breaks can negatively affect the LSTM accuracy. In such cases, traditional methods present a valuable alternative. Further, results suggest that, despite their flexibility and efficiency, machine learning models can benefit from prior seasonal adjustment of the data. Outcomes of this research are valuable for guiding practitioners and can lead to a forecast error reduction of around 70% in some cases. The third study focuses on the renewable energy market and explores two machine learning applications. The demand for renewable energy has been growing exponentially as the world population becomes more conscious about the importance of a sustainable future. Accordingly, renewable energy companies have been attracting the attention of investors. First, machine learning v is employed for Natural Language Processing (NLP) to extract investor sentiment from social media. Subsequently, this information, combined with other covariates, is used as input for forecasting the return and volatility of renewable energy companies' stocks. The analyses include non--trading hours information and the hybridization of state-of-the-art deep learning algorithms. Findings suggest that there is a strong nonlinear relationship between the sentiment information and the dependent variables that is processed more efficiently using the hybrid deep learning approach. Investor sentiment significantly improves forecast performance and contains valuable information not included in standard financial market control variables. Machine learning models are often criticised for their lack of transparency and black box nature. Therefore, the fourth chapter addresses a novel topic in machine learning implementation concerned with the interpretation and explanation of these methods. The study implements a recently proposed methodology for ML interpretation based on cooperative game theory. The SHAP values approach allows the identification of the most important variables considered in an ML prediction model. The methodology is employed to analyse a complex socioeconomic issue in Brazil, that is, the relationship between Information and Comnunication Technologies (ICTs) and people's income. Society is undergoing a rapid process of digitalization and ICTs have a great impact on everyone's personal and professional lives. Results shows that there are complex nonlinear relationships between ICTs and individuals' income, as well as nonlinear interactions between covariates that impact the dependent variable. The ML interpretation approach successfully reveals complex patterns and provides a more detailed understanding. Outcomes of this work present valuable guidelines for the development of public policies such as the promotion vi of high-quality education and digital literacy of the population and the need for more efficient actions to fight gender inequality in the country. Combined, the four studies address main economic segments and present several significant contributions. The applications demonstrated in this thesis are important for the development of machine learning implementation within economics. Machine learning and traditional econometric models have their strengths and weaknesses and are equally valuable. However, the flexible data-driven nature of ML algorithms and their ability to model nonlinear relationships while accounting for interactions among variables is highly advantageous. Machine learning methods have great potential to solve complex economic problems and are a valuable alternative for econometricians as these methods are becoming more transparent and interpretable.