Forecasts Of Gross Domestic Product Research Articles

Application of Linear Regression in GDP Forecasting

Gross Domestic Product (GDP) is the core indicator of national economic accounts, which reflects the economic capacity of a region. It is necessary to have an accurate forecast of GDP. GDP prediction plays an important role in formulating economic policies. By predicting the growth trend of GDP, the government can make correct adjustments and regulate economic activities. This paper takes Guangdong Province and Shandong Province as examples, aiming to establish their GDP models respectively, and obtains their important economic factors on the basis of the models. Firstly, the correlation test and stepwise regression method were used to screen out the variables with significant impact on GDP, and then ridge regression was used to solve the multicollinearity to make the model more accurate. Finally, the similarities and differences of important economic factors in the two provinces were compared. The important economic factors of these two provinces can reflect the characteristics of the economic development of China's north and south, which is conducive to promoting the coordinated development of regional economy and win-win cooperation.

Comparison Methods of Machine Learning and Deep Learning to Forecast The GDP of Indonesia

The success of an economy can bring significant benefits to a country and its society. One way to measure economic growth is by looking at the value of gross domestic product (GDP). The value of a country's GDP is influenced by many factors, including inflation, exports, and imports. Therefore, predicting future economic growth requires forecasting the value of GDP. GDP forecasts are crucial as they provide information about the economic development of a country over a specific period of time. By forecasting GDP, governments and investors can make informed decisions to optimize profits or minimize risks when investing or doing business in a country. This research aims to forecast Indonesia's GDP for the second, third, and fourth quarters of 2023 using the best models from machine learning or deep learning methods. Forecasts are made for each method with and without additional variables. The results indicate that the SimpleRNN algorithm from deep learning without additional variables is the method with the smallest RMSE and MAPE for GDP forecasting. Therefore, the best method used to forecast GDP is the SimpleRNN algorithm, and the forecasted GDP values for Indonesia's second, third, and fourth quarters are 5.350.840,00, 5.483.895,00, and 5.610.077,50 billion rupiah.

The Application of Exponential Smoothing in GDP Forecasting

GDP forecasting is the estimation of gross domestic product (GDP) over a period of time in the future, which is important for national governments, businesses, and investors respectively. The exponential smoothing method, also known as an exponentially weighted average method, is a forecasting method with the advantages of a clear process and convenient calculation. In this paper, the literature method is used, and "gross domestic product", "GDP", and "exponential smoothing method" are used in various databases and literature review websites, "Macroeconomics" and "forecasting" as the keywords to search, comb the literature, categorize and summarize the application of exponential smoothing method in GDP forecasting, and at the same time, the experimental method is used to design an experiment to forecast the future growth of GDP in the next three years by using the data published by the National Bureau of Statistics of China (NBSC). The results of the experiment are that the value-added of the primary industry is expected to continue to grow from about 9227.246 billion Yuan in 2023 to about 100139.20 billion Yuan in 2025. The value added of the secondary industry is projected to continue to grow from about 510,795,800,000,000 Yuan in 2023 to about 563,968,676,000,000 Yuan in 2025. The value added to the tertiary sector is projected to continue to grow from about 679,630.84 billion yuan in 2023 to about 753,204.88 billion yuan in 2025. Correspondingly, the Gross Domestic Product (GDP) will continue to grow, from about 1,281,982,888 million yuan in 2023 to about 1,417,312,284 million yuan in 2025. By analyzing the forecast results, it can be seen that all three major industries in China will maintain growth in the next three years. Depending on the different attributes of the three major industries, the government or enterprises will take appropriate measures to ensure the growth of the industries as well as to cope with various risks. This forecast is based on past data, but the actual economic situation may be affected by many external factors such as the international trade environment and the global economic situation. Governments and businesses will therefore need to pay close attention to these factors in order to adjust their strategies in a timely manner.

Raiders of the lost high‐frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting

SummaryWe introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produce for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these data to study whether the staff forecasts efficiently. Prespecified regressions of forecast errors on forecast revisions show the staff's GDP forecasts exhibit time‐varying inefficiency between FOMC meetings, and also show some evidence for inefficient inflation forecasts.

Forecasting the development of the domestic economy by the system of artificial intelligence, assessment of the risk and stability of the financial sector

The article considers theoretical approaches to forecasting the development of the domestic economy and the stability of the fi nancial sector. The work uses an artificial intelligence system developed by the authors. The paper puts forward and proves the hypothesis that the use of the risk assessment of the financial sector calculated using the VaR model in the neural network AI system can be used to obtain the forecast value of the country’s GDP in order to provide support for managerial decision-making under conditions of uncertainty. Of great importance is the study of the theoretical foundations for increasing the stability of the Russian economy in modern conditions. The use of neural network models in GDP forecasting is an important tool for assessing and ensuring the sustainability of the Russian economy. The novelty of the study lies in the fact that an approach is proposed to fill the gap that concerns the problem of the lack of a reliable approach in assessing the risk of financial sector stability in order to ensure the sustainable development of the domestic economy. Get a correct forecast of the gross domestic product (GDP) in the face of market uncertainty. According to the VaR model, with a 99 % probability, the absolute value of the fi nancial risk of a reduction in the profits of the banking system of the Russian Federation may amount to 108.5 billion rubles in 2022, or 4.5226 %, and the amount of profi t may reach 2.3 trillion rubles. rub. The resulting neuroforecast of the value of GDP for 2022 is 79 023.4 billion rubles. 33.3 % higher than the 59 262.95 billion rubles, declared by experts, the total forecast value of GDP for 1Q. (28410) and 2 sq. (30853). To compare the accuracy of the forecast, it remains to wait until the actual values of GDP become known. The practical significance of the study lies in the fact that in the course of the study the prerequisites for solving an important national economic problem were formed — forecasting the value of GDP and ensuring the sustainable development of the country’s economy.

Covid-19 Impact on Gross Domestic Product (GDP)

One compromise from it is the financial expense to present these actions. This paper assesses the immediate expenses for monetary action while the actions are set up. This paper resolves the inquiry whether crosscountry contrasts in GDP advancement over the first and second quarter of 2020 are generally determined by contrasts in the force with which states enacted restrictions, such as lockdowns, i.e. by the economic effects of mandatory social distancing, or largely by differences in the fatality rate associated with the virus, i.e. by the economic effects of social distancing. The assumptions on the spread of the disease are not grounded in projections, they do not take into consideration the quality of the health systems in the affected countries, transport associations with impacted nations, and wellbeing strategy reactions to the flare-up. The model integrates the decrease popular because of diminished creation and salaries yet doesn't completely catch the free compression sought after, with the exception of the decreases in the travel industry and different administrations that require close human contact. It additionally does exclude the decrease in financial backer certainty and any monetary repercussions. This examination will advance as we tweak suspicions in accordance with early effects and assess possible situations of the spread of the infection. Economic models can be used to model the consequences of pandemics, we consider two scenarios: a global pandemic and an amplified global pandemic. Financial models can be utilized to demonstrate the results of pandemics, we think about two situations: a worldwide pandemic and an intensified worldwide pandemic. On account of the worldwide pandemic, it is accepted that nations bear only one-half of the effect of the full China shock. On account of the enhanced worldwide pandemic, the shocks are uniform across all nations. A pattern worldwide pandemic situation sees GDP of the world fall by 2% underneath the standard, of agricultural nations by 2.5 percent, and of modern nations by 1.8 percent. The downfalls are almost two times as huge in an enhanced pandemic situation in which regulation is accepted to take more time. Furthermore, our granular dataset is broken down at the level of industrial activity within countries, which allows us to make GDP forecasts at the county level. We foster a progression of models to comprehend the reason why rich economies spend essentially more per capita. Results demonstrate that the bundle per capita is emphatically connected with the COVID-19 passings for every 100,000 populace starting around 15 June 2020, populace of something like 65 years of age as percent of all out populace, and pay and salaried specialists as percent of complete business; and conversely connected with independently employed as percent of absolute work, and weak work as percent of all out business. We utilize this data to contrast genuine with anticipated bundles.

A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy.

Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country's or region's future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

The costs of COVID-19 and the cost-effectiveness of testing

Purpose This paper aims to provide an estimation of the costs of the coronavirus (COVID-19) pandemic with a special focus on Spain. Costs include macroeconomic costs of foregone gross domestic product (GDP) attributable to the pandemic and the direct and indirect costs of prevention, treatment and lost productivity. This study also analyzes the cost-effectiveness of the test-tracking-quarantine (TTQ) strategy in Spain. Design/methodology/approach The macroeconomic costs of foregone GDP attributable to the pandemic are estimated for different countries and areas by comparing the present GDP forecasts for 2020 and 2021 with counterfactuals estimated before the COVID-19 crisis aftermath. The total cost of the COVID-19 for Spain in 2020 was obtained using the cost of illness approach with a bottom-up process. A cost-effectiveness analysis of the TTQ strategy in Spain is based on the estimation of the total costs of TTQ and the health gains and avoided health-care costs associated with the TTQ strategy. A sensitivity analysis explores the consequences of uncertainty in key parameters. Findings The GDP cost of the COVID-19 is by far larger than all the other components of the cost. The global cost of the Covid-19 crisis in 2020–2021 is estimated at 14% of 2019 GDP (around 12,206 mm$). In the specific case of Spain, it amounts to 24% of the 2019 GDP; which is 397.3 m €. Spain is and will be by far the European country most economically affected by the pandemic. In Spain 2020, the GDP cost accounts for 94.7% of the total cost of the COVID-19 and health-care direct costs are only 2.14%. TTQ is a dominant strategy in Spain. For each euro spent on it, 7 euros will be recovered only in terms of saved health-care resources. Research limitations/implications Given the large degree of uncertainty and the fast-evolving nature of the epidemic, a number of assumptions are required to arrive at the estimates provided in this study. The results were found to be robust to the assumptions applied. Practical implications TTQ is a key strategy for the contention of the epidemy and it is justified from the economic perspective. Originality/value This is the first estimation of the cost of the COVID-19 and the cost-effectiveness of the TTQ strategy for Spain.

Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting

We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produces for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these new data to study whether the staff forecasts efficiently and whether efficiency, or lack thereof, is time-varying. Prespecified regressions of forecast errors on forecast revisions show that the staff's GDP forecast errors correlate with its GDP forecast revisions, particularly for forecasts made more than two weeks from the start of a FOMC meeting, implying GDP forecasts exhibit time-varying inefficiency between FOMC meetings. We find some weaker evidence for inefficient inflation forecasts.

Модифікація структури моделі Гоша в міжгалузевому аналізі

The current Ghosh model is based on the use of value-added forecast data. The forecasts of gross domestic product and value added have long and regularly been developed by different national and international economic and financial structures, including governmental ones. The level of methods and accuracy of such forecasts is quite high as compared with the final demand forecasts on which the Leontief model is based. Therefore, from the econometric point of view, the accuracy of predictions of output made by using the Ghosh model should be at least not worse than that provided by the classical Leontief model. The modified Ghosh model formally differs from its current model by the presence of a new matrix. However, this difference is only a structural feature, and in mathematical terms these models are identical. At the same time, the modified Ghosh model is more attractive and promising than the current one due to the following factors. It uses one matrix instead of two matrices that appear in the current model. The modified model has a structure (unlike the current one) similar to the structure of the classical Leontief model. Due to this, the modified model is more understandable and easy to use. However, the most important feature lies in the fact that the use of a new matrix significantly expands the possibilities of theoretical research within the input-output structures. Due to constructing a new matrix in the modified Ghosh model, new relations between the vectors of final demand and value added were discovered, which can be efficiently used in balancing the system of input-output matrices. It was also established that the corresponding matrices of the classical Leontief model and the modified Ghosh model have identical diagonal elements in pairs, and this is useful in various analytical studies. Keywords: modified Ghosh model, input-output, Leontief model, value added, final demand

Modelling and forecasting GDP using factor model: An empirical study from Bosnia and Herzegovina

Abstract In the most developed countries the first estimations of Gross Domestic Product (GDP) are available 30 days after the end of the reference quarter. In this paper, possibilities of creating an econometric model for making short-term forecasts of GDP in B&H have been explored. The database consists of more than 100 daily, monthly and quarterly time series for the period 2006q1-2016q4. The aim of this study was to estimate and validate different factor models. Due to the length limit of the series, the factor analysis included 12 time series which had a correlation coefficient with a quarterly GDP at the absolute value greater than 0.8. The principal component analysis (PCA) and the orthogonal varimax rotation of the initial solution were applied. Three principal components are extracted from the set of the series, thus together accounting for 73.34% of the total variability of the given set of series. The final choice of the model for forecasting quarterly B&H GDP was selected based on a comparative analysis of the predictive efficiency of the analysed models for the in-sample period and for the out-of-sample period. The unbiasedness and efficiency of individual forecasts were tested using the Mincer-Zarnowitz regression, while a comparison of the accuracy of forecast of two models was tested by the Diebold-Mariano test. We have examined the justification of a combination of two forecasts using the Granger-Ramanathan regression. A factor model involving three factors has shown to be the most efficient factor model for forecasting quarterly B&H GDP.

Including News Data in Forecasting Macro Economic Performance of China

In this paper we report results of 3- and 6-months ahead forecasts of Gross Domestic Product (GDP) of China. In total we use 124 predictors from various sources and dates ranging from 2000 through 2017. We use China specific macroeconomic time series data and a large number of predictor variables. In our study we follow the latest state of the art, as outlined by, [Stock and Watson, 2016] who use principal component analysis (PCA) to reduce number of variables and apply dynamic factor model (DFM) to make predictions. The results suggest that including news sentiment significantly improves forecasts and this approach outperforms univariate autoregression. The contributions of this paper are two fold, namely, the use of news to improve forecasts and superior forecast of China's GDP.

Structural Breaks, Biased Estimations, and Forecast Errors in a GDP Series of Canada versus the United States

A structural break was suspected for the Canadian gross domestic product (GDP) time series when the reporting system switched from the Standard Industrial Classification system to the North American Industry Classification System system in 1997, as was previously detected for the United States. Any failure to identify in-sample breaks not only will produce biased parameter estimates but may adversely affect the model’s out-of-sample forecasting performance. This study investigated the possibility of poor forecast performance and biased estimation in the presence of the 1997 structural break in Canadian GDP. We confirmed the detected break in Canadian GDP data (1973–2014). All statistics indicated that the coefficients were not stable over time. Three models were employed to provide more accurate forecasts of GDP. The results demonstrate gains in forecasting precision when out-of-sample models accounted for structural breaks. Decision and policy makers might benefit from more precise GDP anticipation if the models were corrected for the 1997 break.

Retraction Note: Prediction of economic growth by extreme learning approach based on science and technology transfer

The purpose of this research is to develop and apply the extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. Economic growth may be developed on the basis on combination of different factors. In this investigation was analyzed the economic growth prediction based on the science and technology transfer. The main goal was to analyze the influence of number of granted European patents on the economic growth by field of technology. GDP was used as economic growth indicator. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and using several statistical indicators. Coefficient of determination for ELM method is 0.9841, for ANN method it is 0.7956 and for the GP method it is 0.7561. Based upon simulation results, it is demonstrated that ELM can be utilized effectively in applications of GDP forecasting.

Consumer Confidence Index in Vietnam: Scale Development and Compilation Methods

A consumer confidence index (CCI) is an important economic indicator which is used to adjust the forecasting of gross domestic product (GDP) and consumer price index (CPI) in the shortterm. Although there exists standard guidelines from the United Nations Statistics Division and European Commission, international experience shows the scale that measures a CCI and the methods of calculating a CCI need to be adapted to the country specific context. Using its own data from the nationally representative survey and factor analysis methods, this paper constructs a scale to measure consumer confidence for Vietnam. The paper, then, computes a CCI and proposes the most appropriate method corresponding to the Vietnamese setting. Validation methods from the paper show that the Vietnamese CCI calculated in the paper reflects approximately the economic picture of the whole country as well as six regions of Vietnam, ensuring the validity of using this index to adjust short-term GDP and CPI forecasts.

Global Economic Burden of Diabetes in Adults: Projections From 2015 to 2030.

Despite the importance of diabetes for global health, the future economic consequences of the disease remain opaque. We forecast the full global costs of diabetes in adults through the year 2030 and predict the economic consequences of diabetes if global targets under the Sustainable Development Goals (SDG) and World Health Organization Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020 are met. We modeled the absolute and gross domestic product (GDP)-relative economic burden of diabetes in individuals aged 20-79 years using epidemiological and demographic data, as well as recent GDP forecasts for 180 countries. We assumed three scenarios: prevalence and mortality 1) increased only with urbanization and population aging (baseline scenario), 2) increased in line with previous trends (past trends scenario), and 3) achieved global targets (target scenario). The absolute global economic burden will increase from U.S. $1.3 trillion (95% CI 1.3-1.4) in 2015 to $2.2 trillion (2.2-2.3) in the baseline, $2.5 trillion (2.4-2.6) in the past trends, and $2.1 trillion (2.1-2.2) in the target scenarios by 2030. This translates to an increase in costs as a share of global GDP from 1.8% (1.7-1.9) in 2015 to a maximum of 2.2% (2.1-2.2). The global costs of diabetes and its consequences are large and will substantially increase by 2030. Even if countries meet international targets, the global economic burden will not decrease. Policy makers need to take urgent action to prepare health and social security systems to mitigate the effects of diabetes.

Appraisal of artificial neural network for forecasting of economic parameters

Abstract The main aim of this research is to develop and apply artificial neural network (ANN) with extreme learning machine (ELM) and back propagation (BP) to forecast gross domestic product (GDP) and Hirschman–Herfindahl Index (HHI). GDP could be developed based on combination of different factors. In this investigation GDP forecasting based on the agriculture and industry added value in gross domestic product (GDP) was analysed separately. Other inputs are final consumption expenditure of general government, gross fixed capital formation (investments) and fertility rate. The relation between product market competition and corporate investment is contentious. On one hand, the relation can be positive, but on the other hand, the relation can be negative. Several methods have been proposed to monitor market power for the purpose of developing procedures to mitigate or eliminate the effects. The most widely used methods are based on indices such as the Hirschman–Herfindahl Index (HHI). The reliability of the ANN models were accessed based on simulation results and using several statistical indicators. Based upon simulation results, it was presented that ELM shows better performances than BP learning algorithm in applications of GDP and HHI forecasting.

Application of artificial neural network with extreme learning machine for economic growth estimation

Abstract The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.

Prediction of GDP growth rate based on carbon dioxide (CO2) emissions

The environment that governs the relationships between carbon dioxide (CO2) emissions and gross domestic product (GDP) changes over time due to variations in economic growth, regulatory policy and technology. The relationship between economic growth and carbon dioxide emissions is considered as one of the most important empirical relationships. However, rigorous economic causal analysis of the tradeoff between carbon dioxide (CO2) emissions and economic growth for credible climate change policies is still limited. The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to predict GDP based on CO2 emissions. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Coefficients of determination for ELM, ANN and GP methods were 0.9271, 0.8756 and 0.4475, respectively. Based upon simulation results, it is demonstrated that ELM can be utilized effectively in applications of GDP forecasting.