Economic Forecasting Research Articles

Machine Learning Integration in Econometric Models

The integration of machine learning (ML) into econometric models represents a transformative advancement in the field of econometrics, enabling researchers to tackle complex, high-dimensional datasets while maintaining the interpretability and rigor of traditional econometric approaches. This research investigates the synergies between machine learning and econometrics, focusing on how ML techniques can enhance model flexibility, predictive accuracy, and causal inference in economic analysis. By leveraging methods such as regularization, ensemble learning, and deep learning, the study explores applications in macroeconomic forecasting, policy evaluation, and market analysis. Furthermore, it addresses the challenges of balancing interpretability with predictive performance, emphasizing the need for hybrid frameworks that merge machine learning's adaptability with econometrics' theoretical foundation. The findings demonstrate the potential of ML-enhanced econometric models to revolutionize economic research and policy-making by providing robust, data-driven insights.

Leveraging artificial intelligence in economics and finance: Enhancing decision-making and market efficiency

Abstract. Artificial Intelligence (AI) has revolutionized the fields of economics and finance by providing advanced tools for decision-making, predictive analysis, and market efficiency. This paper examines the integration of AI technologies such as machine learning and natural language processing with mathematical models, specifically ARIMA for economic forecasting, Black-Scholes for option pricing, and logistic regression for credit risk assessment. By enhancing these models with AI, we demonstrate significant improvements in prediction accuracy and decision-making capabilities. Case studies illustrate a 15% improvement in GDP growth prediction accuracy, a 20% reduction in option pricing errors, and a 20% decrease in credit default rates. The paper also discusses the future prospects of AI, including advancements in quantum computing and ethical considerations like data privacy and algorithmic bias. Implementation challenges such as high costs and data integration issues are addressed, providing a comprehensive roadmap for organizations to effectively leverage AI. This study underscores the transformative potential of AI in shaping the future of economic and financial landscapes, driving innovation and operational efficiency.

AI-Powered Decomposition Techniques for Economic Forecasting

AI-Powered Decomposition Techniques for Economic Forecasting

Economic Forecasting With German Newspaper Articles

Economic Forecasting With German Newspaper Articles

ASSERING THE IMPACT OF MACROECONOMIC INDICATORS ON CORPORATE FINANCIAL PERFORMANCE: AN EMPIRICAL ANALYSIS IN ASIA

This study investigates the impact of macroeconomic indicators, specifically GDP growth and inflation, on corporate financial performance in selected ASEAN countries (Indonesia, Malaysia, the Philippines, Singapore, and Vietnam) from 2010 to 2022. Using data sourced from the OSIRIS database and the World Bank's World Development Indicators, the analysis focuses on three key financial metrics: Return on Assets (ROA), Net Profit Margin (NPM), and Market Capitalization (MC). Employing the Generalized Least Squares (GLS) method, the study addresses heteroskedasticity and autocorrelation in panel data, incorporating control variables such as Total Assets, Current Ratio, and Interest Coverage. Results indicate that GDP growth positively correlates with ROA and NPM, underscoring the importance of economic expansion for corporate efficiency and profitability. However, GDP growth does not significantly impact Market Capitalization, highlighting potential differences in market valuation dynamics. Inflation positively affects ROA and NPM, suggesting firms effectively manage inflationary pressures but does not significantly affect Market Capitalization. The findings provide actionable insights for financial managers, investors, and policymakers. Companies should integrate macroeconomic forecasts into business planning and adopt strategies to mitigate inflationary impacts. Future research directions include sector-specific analyses, longitudinal studies, and the incorporation of additional macroeconomic variables to deepen the understanding of these relationships.

Anticipating prosperity: a systemic analysis of long-term economic trajectories

PurposeThis study introduces an innovative approach to long-term economic forecasting by integrating anticipatory governance (AG) and causal layered analysis methodologies. Focussing on emerging economies, with Vietnam as a case study, we explore potential economic trajectories to 2050.Design/methodology/approachThe research employs a mixed-method approach, combining quantitative economic projections with qualitative scenario building and analysis.FindingsOur findings reveal four distinct future scenarios, ranging from low growth to transformative change. The preferred scenario, characterized by adaptive change, projects a Gross National Income (GNI) per capita of $30,684 by 2050, with significant reductions in agricultural labour and improvements in human development indicators.Originality/valueThis study contributes to theoretical and practical domains by demonstrating the value of integrated foresight methodologies in economic planning. It offers policymakers a comprehensive framework for navigating complex, long-term economic challenges and opportunities. This research underscores the importance of adaptive governance and systemic thinking in achieving sustainable, inclusive economic growth in an increasingly uncertain global environment.

Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.

Fiscal Sustainability in India: an Assessment in Pre and Aftermath of Covid-19

The purpose of this research paper is to evaluate India's budgetary sustainability both before and after the COVID-19 pandemic. Global economies have been severely affected by the COVID-19 pandemic, and India is no exception. The country is facing significant fiscal issues as a result of rising costs for social security, health care, and economic stimulus plans. This study examines shifts in India's fiscal situation prior to the pandemic and the consequences encountered in its aftermath by examining fiscal variables such as state debt, budget deficits, and revenue trends. Using a mixed-method approach, the study combines qualitative evaluations derived from government reports and economic forecasts with quantitative data analysis. Initial results show that there has been a decline in fiscal indicators since COVID-19, which has been made worse by lower revenue generation and higher demands for expenditure.

The Bond Market: A Pillar of Risk Management and Economic Stability

This essay investigates the utility of the bond market in risk management, corporate finance, and economic forecasting. It explores how the bond market’s inherent characteristics of safety, stability, and liquidity make it a crucial component of the global financial system. By examining the issuance of debt instruments, such as bonds, and their role in maintaining corporate control while accessing capital, the essay highlights the advantages of debt financing. Moreover, it underscores how these attributes help corporations manage financial risks and optimize their capital structure. Furthermore, the bond market’s predictive power is analyzed through key indicators like the yield curve and credit spreads, which provide early warnings of economic downturns. The yield curve, in particular, is known for its ability to forecast recessions when it inverts. Credit spreads, the difference between yields on corporate bonds and government securities, also serve as a barometer for economic health and investor sentiment. To bring these theoretical insights into a practical context, the essay includes case studies, such as Apple Inc.’s strategic use of debt. Apple's case demonstrates how a company can leverage debt to finance growth while preserving equity and maintaining financial flexibility. These examples illustrate the tangible benefits and strategic importance of the bond market in corporate finance. The essay concludes with suggestions for leveraging the bond market’s predictive capabilities, emphasizing the need for continuous monitoring and analysis of bond market signals to inform economic and financial decision-making. It also explores future research directions in the evolving financial landscape, considering how technological advancements and regulatory changes may impact the bond market's role in global finance.

PREDICTION OF TIN EXPORTS, POPULATION, POVERTY, AND LABOR FORCE IN THE PROVINCE OF BANGKA BELITUNG ISLANDS

The COVID-19 virus has also caused shocks to the Bangka Belitung Islands Province in various sectors, especially the economy. To overcome this problem, of course the government has prepared responsive policies, both fiscal and monetary policies to prevent post-COVID-19 risks, especially in the economic recession. To prevent a post-COVID-19 economic recession, a prediction or time series forecast is needed on four variables that influence the economic recession, namely the number of tin exports, population, poverty and labor force in the Bangka Belitung Islands Province so that economic growth is maintained. This research aims to predict the four research variables by comparing the Moving Average and Exponential Smoothing methods. This research also uses Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as indicators of model accuracy. Based on the results of the accuracy indicators of this model, it was found that the Exponential Smoothing method was better than the Moving Average method. The predicted results for the value of tin exports in 2024 are -3.3645811 with The RMSE value is 42293770, MAE is 29558091, and MAPE is 84.46131. The negative value in the tin export prediction means that the decline in the value of tin exports in 2024 will not have a significant effect because it is still within a reasonable figure. The total labor force in 2024 will be 11057.23 with RMSE value is 16536.48, MAE value is 14194.02, and MAPE is 112.8078. Then for population the predicted result is 21241.92 with RMSE is 19537.82, MAE is 11548.41, and MAPE is 37.51894. Then for the predicted results the number of poverty is 70.22749 with RMSE, MAE, and MAPE respectively of 3992.146, 3205.528, and 139.1129. The alpha value used is 0.0183.

A Case Study: Comparison of LSTM and GRU Methods for Forecasting Oil, Non-Oil, and Gas Export Values in Indonesia

This study explores the forecasting of Indonesia’s oil, non-oil, and gas export values, highlighting its critical role in supporting national economic growth. Given the inherent volatility in export values, accurate forecasting is vital for informed economic decision-making. The research employs Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, both well-regarded for their ability to handle sequential data and complex temporal patterns. Model performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The findings indicate that although both models produced nearly identical MAPE values of 99.99% across the oil, non-oil, and gas sectors, the GRU model outperformed the LSTM model with RMSE values of 0.0655 for oil and gas exports and 0.0697 for non-oil and gas exports. Moreover, the GRU model’s forecasts align closely with data from the Central Bureau of Statistics (BPS), which reported an 11.33% decline in Indonesia’s export values by the end of 2023. These results suggest that the GRU model not only offers greater accuracy but is also applicable to other economic forecasting contexts, such as exchange rate and inflation predictions, thereby enhancing economic policy-making.

Global Stock Markets during Covid-19: Did Rationality Prevail?

This study assesses the validity of the Efficient Markets Hypothesis (EMH) during the Covid-19 period by evaluating whether various stock markets around the world accurately predicted economic performance in their respective countries. The underlying premise is that stock prices should discount future company cash flows, and the projection of these cash flows can be proxied by investors' ability to forecast macroeconomic conditions. We assess stock market performance from January 2020 to February 2022 in 16 countries, equally split between developed and emerging markets. While results varied, the study suggests that stock markets generally predicted economic activity during Covid-19. The modeling test indicated that stock markets significantly predicted economic activity in 11 out of 16 countries, particularly in developed markets. Furthermore, incorporating the stock market as a variable, improved economic forecasts in all 16 countries. Unlike other studies that mainly focused on the characteristics of share price movements, this research judges the rationality of stock markets against an external criterion. Ultimately, it suggests that EMH was mostly vindicated during the Covid-19 period.

Impact of Energy Prices and Macroeconomic Variables on GDP Prediction UK: Machine Learning Approach

Gross Domestic Product (GDP) is one of the critical indicators of an economy. This study aims to predict the GDP of the United Kingdom using vital macroeconomic variables from 1990 to 2018 as predictors, which include energy prices, unemployment rate, Real Effective Exchange Rate (REER) inflation and net migration. Several machine learning models, namely Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machines (GBM), were implemented, analysed and compared. The models were trained on both scaled and unscaled data, with hyperparameter tuning applied to optimise performance. The models’ performances and accuracy were analysed by employing evaluation metrics Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). As per the findings, after hyperparameter tuning, the SVR model performed best in GDP prediction, followed by GBM. The results of this study underscore the critical role of macroeconomic variables in GDP prediction while highlighting the potential of machine learning models to produce valuable and reliable insight into economic forecasting.

The fed information shocks and the market for corporate control: Predictive and causal effects

We show that contractionary monetary shocks, when reflecting a positive macroeconomic assessment by the Federal Reserve (hereafter “Fed”), predict an economic environment that is characterized by (a) a rise in M&A activity, (b) a higher likelihood of M&A completion, (c) higher bidder gains, (d) limited concerns about M&A overpayment, and (e) higher premia offered by foreign bidders to U.S. targets. Further, Fed information shocks have a standalone and direct causal effect on market expectations of M&A gains. That is, positive Fed information shocks trigger a positive revaluation of pending M&A. This revaluation effect, which holds after controlling for macroeconomic conditions and changes in economic forecasts, is more pronounced in deals that are relatively large, financed with stock, and have received a negative market reaction at the M&A announcement period. Overall, our results highlight the independent and credible signaling role of the Fed in the realm of M&A.

The Integration of Machine Learning in Central Banks: Implications and Innovations

The effectiveness of using artificial intelligence (AI) techniques to lessen the negative effects of economic cycles is examined in this study article. Economic cycles, which are defined by variations in the level of economic activity, present important difficulties for decision-makers in government, business, and society at large. This research investigates many approaches to reducing economic cycles, such as forecasting, policy creation, and adaptive decision-making, by utilizing AI, namely machine learning algorithms. The first section of the paper reviews the body of research on economic cycles and how artificial intelligence might be used to address them. After that, it explores certain AI methods including neural networks, time series analysis, and natural language processing, explaining how they might be used for risk management and economic forecasting. Additionally, the study investigates AI-driven policy interventions, examining how machine learning algorithms might be used to optimize monetary and fiscal policies in order to sustain growth and prevent economic downturns. The study also addresses the difficulties and constraints that come with using AI technologies in economic management, such as problems with algorithmic biases, data quality, and legal considerations. It highlights how crucial interdisciplinary cooperation and openness are to creating AI-driven solutions that are reliable, moral, and socially conscious. This research paper offers insights into the possible advantages and disadvantages of employing AI tools for damping economic cycles through empirical analysis and case studies. In order to fully realize AI's promise in fostering resilience and stability in the economy, it ends by presenting future research areas and policy suggestions. Keywords: artificial intelligence, trend, economic cycles, resilience

Establishment of Agro-Eco Industrial Clusters: A Romanian Perspective

The Romanian economy feels the need to formalize links between the situational economic forecast and the forecast of technological events in industry and agriculture. The article studies a “dimension” of the organization for classifications of data, structures and economic / agro-eco-industrial events for clustering with the help of “dimensionality”. Meta-prognostic relationships between economics, forecasting and forecasting agents (mathematical elements) are described and the role of metricity, sub-metricity and ultra-metricity in the observation of an automatic ontology for classifications supporting agro-eco-industrial / food clustering is highlighted. The paper proposes the algorithm of the informal classes of supervised / unsupervised data about agro-eco-industrial / food companies in Romania in order to establish classifications that would motivate the formation of clusters in the field. It is concluded that the mathematical apparatus for classification must be developed because based on symbolic mathematical models can be developed computer / computer programs for calculations of ultra-metricity of transformations for linearization’s of similarities between firms, eliminating reminiscences / redundancies, aiming to reduce space / distances between data that represent, in fact, enterprises that can enter agro-eco-industrial / food clusters. Keywords: agro-eco-industrial clusters, ultra-metricity, clustering, economic forecasting, classification

The Role of AI-powered financial analytics in shaping economic policy: A new era for risk management and national growth in the United States

The integration of AI-powered financial analytics is revolutionizing how U.S. enterprises and policymakers’ approach economic forecasting and risk management. This article explores the role of AI-based tools, such as machine learning and predictive analytics, in enhancing financial decision-making processes and shaping national economic policy. By providing real-time insights and accurate predictions, AI enables businesses and regulators to anticipate economic trends, mitigate financial risks, and foster financial stability. Through case studies, the research demonstrates how AI has driven economic growth, reduced risks, and enhanced financial oversight within key sectors. Additionally, it investigates the implications of AI on policymaking, highlighting how AI-enabled tools support the development of data-driven economic policies for long-term national growth. Despite the transformative potential, the article also addresses the ethical and regulatory challenges in deploying AI for economic decision-making. Ultimately, this research emphasizes that the future of economic policy in the United States is increasingly tied to the continued advancement and integration of AI-powered financial analytics.

Statistical model validation through white noise hypothesis testing in regression analysis and ARIMA models

In the domain of finance and economics, ensuring the validity and dependability of forecasting models is crucial. In this paper, the White Noise Hypothesis Test is elucidated as an essential model validation tool for both the regression and ARIMA models. In the case of regression analysis, there is a need for residual independence and homoscedasticity to confirm that the estimates of the parameters are not biased, that is, the chances of the parameter estimated to be true is high. In the case of an ARIMA model, residuals are checked for the white noise characteristics, and so as to ensure that all the relevant patterns associated with the time series have been detected. The paper uses simulated stock market data and risk management scenarios to demonstrate how these tests enhance model reliability, since when white noise characteristics are ascertained, the residuals are confirmed random and independent. The importance of validating models is discussed in the context of economic forecasting and policy analysis, where the validated models are indispensable for anticipating a market upheaval or to make right economic decisions. Therefore, when white noise test is used to confirm that the model meets the white noise criteria, this improves the credibility of predictions and thus influences strategic decision making in the uncertain environment.

Prediction intervals for economic fixed-event forecasts

Prediction intervals for economic fixed-event forecasts

On the integration of multiple criteria decision aiding and forecasting: Does it create value in portfolio selection?

A systemic integration of multiple criteria decision aiding (MCDA) and forecasting is presented for enhancing the quality of investment decisions. First, we provide a new methodological approach for combining forward-looking information, as an input to further MCDA analysis and we illustrate that the informational content of the MCDA scores is distinct from that of the original data used as input to the MCDA. Moreover, we provide very compelling evidence that the produced MCDA scores and rankings have incremental information, over and above that being contained in either the original data or the forecasts themselves. Therefore, it can be used successfully in providing methods which will have a high, a priori, chance of resulting in financial performance enhancements; to this end, we have illustrated this finding with a number of different approaches, including asset rotation and portfolio construction. Next, we show that the MCDA ranked variables can be used for both in-sample explaining and out-of-sample forecasting of critical economic variables, such as the real GDP growth or the unemployment rate, so that MCDA might be able to assist in identifying leading indicator properties for economic forecasting. The evaluation of the suggested approach entails extensive empirical testing on the various US economy market sectors, as expressed by the corresponding S&P 500 Exchange Traded Funds (ETFs), for a period of 22 years, documenting specific economic stylized facts, reported for first time in the relevant literature.