GDP Forecasts Research Articles

US: Revisions strengthen conviction in our 2025 GDP forecast

US: Revisions strengthen conviction in our 2025 GDP forecast

Transparency and forecasting: the impact of conditioning assumptions on forecast accuracy

ABSTRACT This study investigates the impact of inaccurate assumptions on economic forecast precision. We construct a new dataset comprising an unbalanced panel of annual German GDP forecasts from various institutions, taking into account their underlying assumptions. We explicitly control for different forecast horizons to reflect the information available at the time of release. Our analysis reveals that approximately 75% of the variation in squared forecast errors can be attributed to the variation in squared errors of the initial assumptions. This finding emphasizes the importance of accurate assumptions in economic forecasting and suggests that forecasters should transparently disclose their assumptions to enhance the usefulness of their forecasts in shaping effective policy recommendations.

The Impact of Oil Prices on the GDP of V4 Countries

This study is concerned with forecasts of GDP growth for the Visegrad countries (V4-hereafter) and oil prices simultaneously. The series for GDP and Oil prices are quarterly, covering the period from December 1, 2000, to October 1, 2023. Neural network techniques were performed to generate individual forecasts. The forecasts for oil prices maintain higher accuracy than the GDP ones due to autoregressive lags. In other words, current oil prices absorb significant influence from past prices. The GDP forecasts for the V4 group indicate tremendous shock and moving growth to a lower steady state. The lower growth observed by the end of the period under review can be attributed to two subsequent non-economic shocks: the Russia-Ukraine war and the COVID-19 pandemic. The V4 countries faced difficulties after the outbreak of COVID-19, mainly due to the stringency measures. In addition to accelerating inflation, the war also disrupted energy commodities within supply chains. These and other facts justify the low growth that awaits these countries until 2028. Ultimately, the oil price until 2028 is estimated to oscillate between $60 and $90 per barrel.

Predicting Analysts’ S and P 500 Earnings Forecast Errors and Stock Market Returns using Macroeconomic Data and Nowcasts

This study scrutinizes the quality of “bottom-up” forecasts of near-term S and P 500 Composite earnings, derived by aggregating analysts’ forecasts for individual firm-level earnings. We examine whether forecasts are broadly consistent with current macroeconomic conditions reflected in economists’ near-term outlook and other available data. To the contrary, we find that a simple macroeconomic model of aggregate S and P 500 earnings, coupled with GDP forecasts from the Blue Chip Survey and recent dollar exchange rate movements, can predict large and statistically significant errors in equity analysts’ bottom-up forecasts for S and P 500 earnings in the current quarter and the quarter ahead. This finding is robust to the requirement that our econometric model is calibrated using only data available at the time of forecast. Moreover, the discrepancy between the macro-model-based earnings forecasts and analysts’ forecasts has predictive power for 3-month-ahead stock returns

Analysis and Forecasting of Energy Data and GDP Based on Pearson Correlation Analysis-Random Forest Algorithm

The relationship between energy data and GDP is an important economic issue because energy is the foundation that supports modern economic development. This paper examines the relationship between energy data and GDP and uses Pearson correlation analysis to calculate the relationship between each variable in energy data and annual GDP for 52 regions. The results show that total electricity consumption is the factor with the highest degree of influence on GDP, followed by natural gas consumption, biomass consumption, hydroelectricity consumption, LPG consumption and geothermal energy consumption. This indicates that there is an extremely strong correlation between local energy data and GDP. To further explore this correlation, this paper attempts to predict the GDP of the locality based on the local energy data using a machine learning approach.The Random Forest algorithm is chosen as the machine learning model, and the training, validation and test sets are divided according to the ratio of 6:2:2. By using MSE, RMSE, MAE, MAPE and R to evaluate the model, it is found that the random forest machine learning algorithm can predict the local GDP well.The predicted value of GDP and the actual value of GDP are very close to each other, meanwhile, the value of MSE is 62.779.These findings show that the local energy data are closely related to economic development. The impact of energy factors on the economy should be taken into account when formulating economic policies. In addition, machine learning algorithms can provide strong support for economic forecasting and provide more accurate information for decision makers.

Revolutionizing economic insights and development: The transformative power of machine learning

This comprehensive study delves into the transformative role of machine learning (ML) in various facets of economic analysis and development. By leveraging ML's advanced predictive capabilities, the paper explores its impact on economic forecasting, the analysis of regional economic disparities, sustainable development, and bridging the digital divide. Through empirical analyses and case studies, we demonstrate how ML algorithms enhance the accuracy of GDP forecasts, identify factors contributing to economic inequality, and foster technological innovations that drive productivity and sustainable practices. The paper also addresses the challenges and limitations inherent in the application of ML, such as data quality, model overfitting, and the interpretability of complex algorithms. By offering targeted policy recommendations, this research contributes to the formulation of more effective economic policies and interventions.

The Relationship between Oil Prices, Gold Prices, the Stock Market, and U.S. GDP

The forecast of GDP has always been a popular research topic today, and its related influencing factors are also complicated. This paper selects three key factors in economic development: gold price, crude oil price, and stock market index, explores their relationship with U.S. GDP, and then uses these three factors as predictive variables to obtain a multiple linear regression equation related to GDP. The research methods of this paper are as follows: Firstly, monthly U.S. GDP, monthly crude oil price, monthly gold price, and monthly S&P 500 index were collected. Secondly, correlation analysis was carried out on these data, including calculation of correlation coefficient and cross-correlation analysis. Correlation analysis showed that GDP had a significant positive correlation with other variables. Then, a multiple linear regression model was established with monthly U.S. GDP as the predicted variable, monthly crude oil price, monthly gold price, and monthly S&P 500 index as the predictor variable. Finally, multiple regression equations are obtained through testing. This multiple regression equation can be used to predict GDP further.

Analysis of Economic Impact Based on Time Series Model -- Taking the GDP of Two Provinces as an Example

Time series analysis is an important branch of statistics, which is widely used in various fields, as well as in the economic field. In the economic field, a reasonable GDP forecast helps a country to set credible, substantive, and strategic goals. In recent years, Northeast China has seen an obvious decline in the national economic ranking, and the relevant GDP data of Jilin and Heilongjiang provinces are more obvious. This paper is based on time series analysis and support vector regression (SVR) algorithm to build a series model. The model was established by using 60 GDP data of Jilin Province and Heilongjiang Province from 1959 to 2022 with similar economic facts. After calculation and verification, the optimal model of Jilin Province is ARIMA (1, 1,0), the SVR with parameter C of 10 and 2 of 0.007, and the combined model with it as the main model is the optimal model of Heilongjiang Province. The GDP of the two provinces will continue to rise in the next five years, and the GDP of Jilin Province will rise faster, and the GDP gap between the two provinces will gradually narrow.

A STUDY ON THE IMPACT OF CRIPPLING CHINESE ECONOMY ON MULTINATIONAL CORPORATIONS: INDIA’S FDI APPROACH

This project explores the multifaceted impact of a crippling Chinese economy on Multinational Corporations (MNCs) and examines their strategic responses. As disruptions in China ripple through supply chains, reshaping market dynamics, and introducing regulatory ambiguities, MNCs must skillfully navigate these challenges to uphold resilience and pursue sustained growth. Concurrently, India also emerges as a potential alternative for MNCs seeking stability and growth opportunities beyond China. This research paper delves into understanding the factors of Chinas economic growth over years, the cause for the downfall, and strategic measures that India can adopt to harness the potential inflow of FDI amidst the evolving global economic landscape, such as policy reforms, infrastructure development, and sector-specific incentives. The analysis section examines the major factors that determine the economy such as GDP growth rate, Population, FDI, Youth unemployment rate, GDP Per capita income and forecast of Indias momentum in FY25 based on secondary data. The conclusion emphasizes the central theme of harnessing Indias vast growth potential while confronting the key challenges that impede its expansion. By implementing strategic policies and precise reforms, India can sustain its robust growth trajectory, surpassing the $5 trillion threshold. KEYWORDS: Economic Landscape, Crippling Chinese economy, Indias measures, Multinational Corporations (MNCs), FDI, Sustained growth, Strategic policies, Challenges.

Construction and Optimization of Macroeconomic Data Forecasting Model Based on Machine Learning

Human capital was first viewed as a production component in macroeconomic development, but endogenous growth theories eventually replaced this perspective. The majority of earlier research used econometric models to investigate the GDP forecasting. Since machine learning models can efficiently resolve nonlinear interactions, this study offers a new perspective by examining the linkages using machine learning approaches. The prediction model for economic development was created for this reason using the best machine learning techniques, specifically the Support Vector Machine. In order to improve SVM prediction, the hyper parameters were optimised using the Bayes approach and several kernel functions. Three statistical metrics—the coefficient of determination, mean absolute error, and root mean square error are used to assess the models' effectiveness.

Macroeconomic forecasting and macroeconomic forecasts

The specificity of macroeconomic forecasts is determined not so much by the list of predicted indicators or by mathematical tools used, but by the unavoidable human factor, which often generates great difference between forecasts made by various professionals. In Russian-language literature, this psychological aspect of macroeconomic forecasting has not received any attention; our work is designed to fill this gap. As a source of statistical data we used the forecasts extracted from the quarterly Poll of Professional Forecasters (PPF), which began in the first quarter of 2000. An analysis of real GDP and CPI forecasts made it possible to identify optimists and pessimists among independent experts, and also to show that the official forecasts by the Russian Ministry of Economic Development and the Bank of Russia are often too optimistic (with the possible exception of forecasts for a current year). It was also confirmed that the accuracy of consensus-forecasts exceeds (in the long run) not only that of official forecasts, but also forecasts made by the vast majority of independent experts. This asserts consensus-forecasts as a benchmark against which macroeconomic forecasts of other experts and organizations should be compared. An analysis of errors for different forecast horizons showed that in the medium term, the most significant ones are associated with underestimation of the resilience of the Russian economy to external shocks. This aspect of macroeconomic forecasting is closely related to forecasting cyclical dynamics (in particular, recessions, their beginning, depth and duration). Currently, this is just what requires additional attention by macroeconomic forecasters.

Short Term Forecasting of Euro Area Gdp Using Common Factors

Short Term Forecasting of Euro Area Gdp Using Common Factors

Macroeconomic uncertainty and non-GAAP disclosure

This study examines the relation between macroeconomic uncertainty and firms’ non-GAAP disclosure. Using the dispersion of GDP forecasts and the Chicago Board Options Exchange (CBOE) VIX Index to proxy for macroeconomic uncertainty, this study finds that non-GAAP reporting likelihood decreases during periods of high macroeconomic uncertainty. However, for firms that choose to disclose non-GAAP earnings, their non-GAAP exclusions become more transitory, indicating an increase in non-GAAP reporting quality. Further evidence suggests that the increase in non-GAAP reporting quality is driven by the decrease in the persistence of excluded special items. Overall, the findings of this study advance the knowledge of how non-GAAP disclosure changes as macroeconomic uncertainty varies.

Does energy consumption play a key role? Re-evaluating the energy consumption-economic growth nexus from GDP growth rates forecasting

Given the strong link between economic growth and energy consumption, we construct a new set of energy consumption indices (ECI) to forecast GDP growth rates. The findings demonstrate the strong predictive power of the new indices. In particular, the electricity consumption index from the industrial sector has an excellent predictive performance and deserves the attention of the government and academia to help formulate economic growth targets. The mixed-frequency model with the Least Absolute Shrinkage and Selection Operator approach (MIDAS-LASSO) enables the full exploitation of energy consumption information for stable forecasting performance, even in the context of crisis fluctuations and geopolitical risk shocks. Furthermore, we apply forecast encompassing tests and variable selection to statistically investigate the predictive information. By analyzing energy consumption characteristics, we contribute to the economic growth hypothesis at the level of energy consuming sectors and provide new insights into GDP forecasting.

Socio-Economic Development of European Countries in Times of Crisis: Ups and Downs

This article analyzes the dynamics of the changes in indicators of socio-economic development under conditions of financial and economic crises and their negative consequences. The study proves that financial crises are associated with severe and prolonged downturns in economic activity. The socio-economic development of European countries in times of crises was analyzed. The cyclical nature of the onset of crises was confirmed via the study of the dynamics of socio-economic development indicators. The main emphasis was on the financial crisis of 2008–2009 and the COVID-19 crisis (2020–2021). The main indicators characterizing the crises were identified based on an analysis of literary sources. Their classification was developed according to the following groups: leading indicators, lagging indicators, and client leading indicators of expansion. Based on the correlation analysis, indicators that have a significant impact on socio-economic development and are predictors of crisis onset were identified. The authors suggest considering such leading indicators as increases in the private credit in the GDP, budget deficit, balance of payment deficit, and real interest rate. The major lagging indicators that have strong correlations with the GDP, such as the employment rate, general government debt, stock price volatility, and investment, were identified. Client leading indicators of expansion include unemployment, an increase in the number of new enterprises, an increase in purchasing power, etc. Some indicators, such as unemployment, can be both lagging indicators and client leading indicators of expansion. The negative consequences of the crisis are caused by the crisis itself as well as by the imbalances preceding the crisis. Therefore, the study of the predictors of crisis onset is relevant for timely decision making in order to prevent the negative consequences of the crisis. Based on the identified lagging indicators, the 2008–2009 crisis and the COVID-19 crisis were studied. To study the development processes of these crises, the authors analyzed by quarters the dynamics of the development of the following macroeconomic indicators: the GDP, employment, and investment levels. The similarities and discrepancies were identified in the natures of the emergences and courses of the 2008–2009 crisis and the COVID-19 crisis using the comparison method. The case study of the Eurozone and individual EU countries (Germany, France, Italy, and Spain) was used. Considering the similar courses of the crises, the forecast of the socio-economic development was made using the analyzed indicators during the COVID-19 crisis based on the 2008–2009 crisis data. The forecast approximation indicators were calculated, and a method for constructing further forecasts was selected. Based on retrospective data, the GDP forecast was developed via the use of the extrapolation method for 2023–2024. It is necessary to consider that while forecasting crises caused by unforeseen events and external influences, it is advisable to use qualitative analysis along with quantitative analysis. This article will be useful to researchers, political elites, experts, and financial analysts when developing programs for the socio-economic development of countries.

The prediction of Chongqing's GDP based on the LASSO method and chaotic whale group algorithm–back propagation neural network–ARIMA model

Accurate GDP forecasts are vital for strategic decision-making and effective macroeconomic policies. In this study, we propose an innovative approach for Chongqing's GDP prediction, combining the LASSO method with the CWOA—BP–ARIMA model. Through meticulous feature selection based on Pearson correlation and Lasso regression, we identify key economic indicators linked to Chongqing's GDP. These indicators serve as inputs for the optimized CWOA–BP–ARIMA model, demonstrating its superiority over Random Forest, MLP, GA–BP, and CWOA–BP models. The CWOA–BP–ARIMA model achieves a remarkable 95% reduction in MAE and a significant 94.2% reduction in RMSE compared to Random Forest. Furthermore, it shows substantial reductions of 80.6% in MAE and 77.8% in RMSE compared to MLP, along with considerable reductions of 77.3% in MAE and 75% in RMSE compared to GA–BP. Moreover, compared to its own CWOA–BP counterpart, the model attains an impressive 30.7% reduction in MAE and a 20.46% reduction in RMSE. These results underscore the model's predictive accuracy and robustness, establishing it as a reliable tool for economic planning and decision-making. Additionally, our study calculates GDP prediction intervals at different confidence levels, further enhancing forecasting accuracy. The research uncovers a close relationship between GDP and key indicators, providing valuable insights for policy formulation. Based on the predictions, Chongqing's GDP is projected to experience positive growth, reaching 298,880 thousand yuan in 2022, 322,990 thousand yuan in 2023, and 342,730 thousand yuan in 2024. These projections equip decision-makers with essential information to formulate effective policies aligned with economic trends. Overall, our study provides valuable knowledge and tools for strategic decision-making and macroeconomic policy formulation, showcasing the exceptional performance of the CWOA–BP–ARIMA model in GDP prediction.

Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)

Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)

Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers

This paper applies a new artificial intelligence architecture, the temporal fusion transformer (TFT), for the joint GDP forecasting of 25 OECD countries at different time horizons. This new attention-based architecture offers significant advantages over other deep learning methods. First, results are interpretable since the impact of each explanatory variable on each forecast can be calculated. Second, it allows for visualizing persistent temporal patterns and identifying significant events and different regimes. Third, it provides quantile regressions and permits training the model on multiple time series from different distributions. Results suggest that TFTs outperform regression models, especially in periods of turbulence such as the COVID-19 shock. Interesting economic interpretations are obtained depending on whether the country has domestic demand-led or export-led growth. In essence, TFT is revealed as a new tool that artificial intelligence provides to economists and policy makers, with enormous prospects for the future.

Crunching the Numbers: A Comparison of Econometric Models for GDP Forecasting in Madagascar

Crunching the Numbers: A Comparison of Econometric Models for GDP Forecasting in Madagascar

Commodity prices and global economic activity

Commodity prices provide useful information about current and future global economic activity. First, we show that overall commodity prices indeed tend to comove with economic activity. Second, we try to extract the global demand factor(s) using many commodity prices. While commodity prices reflect both demand and supply factors, by relying on a wide variety of commodity prices, supply shocks can be filtered out as they tend to be commodity-specific idiosyncratic shocks except for widespread supply disruptions confined to a few historical periods. In this paper, we then show that factors extracted from commodity prices movement contain useful information to nowcast and forecast global GDP and industrial production.