The quest for accurate economic forecasting has traditionally been dominated by econometric models, which most of the times rely on the assumptions of linear relationships and stationarity in of the data. However, the complex and often nonlinear nature of global economies necessitates the exploration of alternative approaches. Machine learning methods offer promising advantages over traditional econometric techniques for Gross Domestic Product forecasting, given their ability to model complex, nonlinear interactions and patterns without the need for explicit specification of the underlying relationships. This paper investigates the efficacy of Recurrent Neural Networks, in forecasting GDP, specifically LSTM networks. These models are compared against a traditional econometric method, SARIMA. We employ the quarterly Romanian GDP dataset from 1995 to 2023 and build a LSTM network to forecast to next 4 values in the series. Our findings suggest that machine learning models, consistently outperform traditional econometric models in terms of predictive accuracy and flexibility

This paper investigates the forecasting performance of COMEX copper futures realized volatility across various high-frequency intervals using both econometric volatility models and deep learning recurrent neural network models. The econometric models considered are GARCH and HAR, while the deep learning models include RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). In forecasting daily realized volatility for COMEX copper futures with a rolling window approach, the econometric models, particularly HAR, outperform recurrent neural networks overall, with HAR achieving the lowest QLIKE loss function value. However, when the data is replaced with hourly high-frequency realized volatility, the deep learning models outperform the GARCH model, and HAR attains a comparable QLIKE loss function value. Despite the black-box nature of machine learning models, the deep learning models demonstrate superior forecasting performance, surpassing the fixed QLIKE value of HAR in the experiment. Moreover, as the forecast horizon extends for daily realized volatility, deep learning models gradually close the performance gap with the GARCH model in certain loss function metrics. Nonetheless, HAR remains the most effective model overall for daily realized volatility forecasting in copper futures.

This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study investigates both the short-term and long-term effects of fiscal benefits on industrial investment. The results confirm the significant role of fiscal incentives in driving long-term industrial growth, while also highlighting the importance of a stable macroeconomic environment, public investment, and access to credit. Machine learning models provide additional insights into nonlinear interactions between fiscal benefits and other macroeconomic factors, such as exchange rates, emphasizing the need for tailored fiscal policies. The findings have important policy implications, suggesting that fiscal incentives, when combined with broader economic reforms, can effectively promote industrial development in emerging economies.

This paper aims to explore the application of machine learning in forecasting Chinese macroeconomic variables. Specifically, it employs various machine learning models to predict the quarterly real GDP growth of China, and analyzes the factors contributing to the performance differences among these models. Our findings indicate that the average forecast errors of machine learning models are generally lower than those of traditional econometric models or expert forecasts, particularly in periods of economic stability. However, during certain inflection points, although machine learning models still outperform traditional econometric models, expert forecasts may exhibit greater accuracy in some instances due to experts' more comprehensive understanding of the macroeconomic environment and real-time economic variables. In addition to macroeconomic forecasting, this paper employs interpretable machine learning methods to identify the key attributive variables from different machine learning models, aiming to enhance the understanding and evaluation of their contributions to macroeconomic fluctuations.

The recent emergence of Large Language Models (LLMs) has reshaped the landscape of natural language processing, ushering in a new era of computational linguistics. Bolstered by advancements in machine learning and deep neural networks, LLMs have garnered widespread attention for their remarkable ability to understand and generate human-like text. This transformative technology has revolutionized various applications, ranging from machine translation and sentiment analysis to chatbots and content generation. By leveraging vast amounts of text data and sophisticated algorithms, LLMs have demonstrated unparalleled proficiency in capturing linguistic nuances, contextual dependencies, and semantic meanings.

Social science research often hinges on the relationship between categorical variables and outcomes. We introduce CAVIAR, a novel method for embedding categorical variables that assume values in a high-dimensional ambient space but are sampled from an underlying manifold. Our theoretical and numerical analyses outline challenges posed by such categorical variables in causal inference. Specifically, dynamically varying and sparse levels can lead to violations of the Donsker conditions and a failure of the estimation functionals to converge to a tight Gaussian process. Traditional approaches, including the exclusion of rare categorical levels and principled variable selection models like LASSO, fall short. CAVIAR embeds the data into a lower-dimensional global coordinate system. The mapping can be derived from both structured and unstructured data, and ensures stable and robust estimates through dimensionality reduction. In a dataset of direct-to-consumer apparel sales, we illustrate how high-dimensional categorical variables, such as zip codes, can be succinctly represented, facilitating inference and analysis.

The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict households' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land-use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.

I've read several studies and articles that claim Econometric models are still superior to machine learning when it comes to forecasting. In the article, "Statistical and Machine Learning forecasting methods: Concerns and ways forward", the author mentions that, "After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined." In many business environments a data scientist is responsible for generating hundreds or thousands (possibly more) forecasts for an entire company, opposed to a single series forecast. While it appears that Econometric methods are better at forecasting a single series (which I generally agree with), how do they compare at forecasting multiple series, which is likely a more common requirement in the real world? In this article, I am going to show you an experiment I ran that compares machine learning models and Econometrics models for time series forecasting on an entire company's set of stores and departments.

A Ph.D Student's guide to Econometrics and Machine Learning Econometric modeling and machine learning can be considered as twin models. While econometric models are statistical models applied in econometrics, machine learning is a scientific field that studies about the formation and analysis of algorithms that can learn from data. Machine learning explained The generation of information is being carried out at a momentous rate nowadays. Being a very effective form of data analysis, machine learning automated analytical model building. It has its roots in artificial intelligence and believes that systems can learn from data.

They can only give you answers." Big data utilization in economics and the financial world has increased with each passing day. In previous reports, we have discussed issues and opportunities related to big data applications in economics/finance. This piece is a quick summary of a more-detailed report that outlines a framework to utilize machine learning and statistical data mining tools in the economics/financial world with the goal of more accurately predicting recessions. Decision makers have a vital interest in predicting future recessions in order to enact appropriate policy.