GDP Trends Research Articles

Application of empirical mode decomposition to improve deep learning for US GDP data forecasting

The application of deep learning methods to construct deep neural networks for the prediction of future econometric trends and econometric data has come to receive a lot of research attention. However, it has been found that the long short-term memory (LSTM) model is unstable and overly complex. It also lacks rules for handling econometric data, which can cause errors in prediction and in the actual data. This paper proposes an empirical mode decomposition (EMD) method designed to improve deep learning for understanding US GDP trends and US GDP data prediction research. The US GDP growth rate is used only for LSTM model prediction and for real data comparison; the root mean squared error (RMSE) is 2.7274. The US GDP growth rate is EMD decomposed to obtain the intrinsic mode functions (IMFs) after which the LSTM model is used to predict an RMSE of 0.93557.

Sustainable economic growth and unemployment nexus of SDG 2030: Bangladesh in Asia.

In this study, we applied the Okun’s law to examine the current progress of sustainable economic growth and unemployment goal of the fourteen developed and developing countries of Asia. Our findings suggests in the long-run, only China has achieved the sustainable economic growth and unemployment goal until 2018. However, due to recent downward trend of economic growth and upward trend of unemployment rate, China is currently walking opposite to the said goal. Besides, Bangladesh, Kazakhstan and Thailand are progressing fast and Malaysia and Tajikistan are progressing moderately towards the sustainable economic growth and unemployment goal 2030. Despite a higher Okun’s coefficient, Turkey is walking opposite to the SDG 2030 due to a sharp rising trend of unemployment and a quick declining trend of GDP in recent time. The rest of the countries are far-off from the above-mentioned goals.

A liquidity crunch in an endogenous growth model with human capital

AbstractThere is by now reasonable evidence that supports the notion of a trend break in the U.S. GDP since the Great Recession. To explain this phenomenon, I construct a version of the Lucas endogenous growth model, amplified with financial frictions and financial disruptions in the firms' sector. I then show how a transitory liquidity crunch is capable, at least qualitatively, of producing a similar pattern of a persistent downward shift in the GDP trend as one could infer happened in the United States since 2008. The main mechanism by which such a result is found relies on workers' decisions on providing labor to firms versus accumulating human capital. I show that a transitory liquidity crunch reduces the demand of labor. Workers anticipating a phase of depressed wages make the decision of accumulating more human capital in the short run, thereby reducing labor supply to firms. In the long run, however, incentivized by a strong recovery, workers decrease human capital accumulation and increase labor supply. Under plausible parameterizations of the model, this situation produces a net effect of a decrease in overall productivity that permanently reduces the trend at which the economy was growing prior to the crisis.

Did Financial Consumers Benefit from the Digital Transformation? An Empirical Investigation

This study aimed to test, through empirical investigation, how the rapid advancement of digital transformation (DT) has impacted the price of financial services. To this end, we compiled a set of macro-level indicators on the aggregate outcomes of the financial services sector in Korea over the last three decades and conducted an analysis to gauge the effects of DT on the country using those indicators. Using the ARDL-ECM (autoregressive distributed lag error-correction model), we show that, over time, the unit cost of financial intermediation in Korea has tended to move in tandem with the growth in economic output, although the profit portion of the unit cost has not exhibited a long-term relationship with the GDP trend. The long-term effect of the DT trend is negative (i.e., cost-saving) for labor input, capital expenditure, and the total unit cost of financial intermediation, which are all shown to be statistically significant. Consequently, we conclude that DT contributed to enhancing consumer benefit, mainly by achieving the operational efficiency of labor and capital, from 1990 to 2019 in Korea. From a policy perspective, our finding implies that DT-driven innovation in the sector can benefit financial customers if excessive levels of profit are restrained through market competition.

High-speed rail to prosperity? Assessing the role of transportation improvement in the urban economy

Investigate the impact of high-speed rail (HSR) on local economy is of great importance and interest to policy makers and scholars. Though there is a big body of literature in this area, the estimates of such impact are inconsistent or even contradictory. The empirical evidence remains problematic for several reasons: endogenous route placement; omitted variable bias; heterogeneity across different regions; various confounding factors. In this paper, we assess this impact by constructing the appropriate counterfactual in the absence of HSR services with similar GDP level and GDP trend before the debut of HSR services. The control group forms a good fit for the treatment group, and the economic performance of the control group was even slightly stronger than that of the treatment group before 2007. Using the DID method, we find the HSR network promoted local GDP by approximately 3.3 percentage points. The introduction of HSR service helped cities attract more industrial enterprises and achieve more industrial output, but its effect on the service sector was not pronounced. Our results are robust to different sample selection procedures, to the dynamic analyses, to different empirical strategies. Our study thus provides new and solid empirical support to the argument that HSR benefits local economic development.

Resilience factors in the Russian economy: The comparative analysis for 2000–2020

Subject. The study investigates the impact of shocks on the economic dynamics during recurring crises. Nevertheless, the impact of exogenous shocks may turn to be minor for GDP of some countries, while they cause unfavorable consequences for the economic development of others. Objectives. I herein identify factors of resilience in the Russian economy, referring to three different crises that took place in 2000–2020, by conducting a theoretical overview of the concept Economic Resilience and resilience factors of economic systems against different shocks. Methods. The decomposition of macroeconomic indicators is the principal method of research. The resilience of the Russian economy and other advanced economies was assessed by macroeconomic indicators related to trends in GDP and the unemployment level. I point out two groups of factors – the innate and acquired (adaptability). Results. During the global financial crisis in 2008, the Russian economy demonstrated its innate factors, such as the availability of considerable reserves and capital mobility between the financial and industrial sectors, as well as adaptive factors as part of the national anti-crisis policy. During the period of sanctions, the resilience of the Russian economy stemmed from the enormous potential of the industrial sector and R&D, considerable reserves for production development, which underlay the import substitution policy. Finally, in the outbreak of the 2020 crisis, Russia managed to handle the crisis much better than the leading countries of the world, since there is a prevalence of State-owned large business and the domestic localization of value added chains. Conclusions and Relevance. The study substantiated the objectivity of resilience factors of the Russian economy during multiple crises, i.e. a combination of financial, commercial, political and pandemic crises.

Households’ credit demand: Main trends and characteristics for Ukraine

Household demand for credits is quite volatile, which requires constant evaluation of it changes. The purpose of the paper is to identify quantitative signals, the use of which increases the predictability of the credit market development. The study utilizes technical analysis methods for an econometric estimation of trends in household demand for credits in Ukraine for the 2002–2019 period. Based on the analysis of historical market lows, it was argued that with all the negative effects of destabilizing factors, the household demand for loans will not fall below the market support point of UAH 50 million. The financial behavior of Ukrainian households when choosing the type of loan is stable and does not change with fluctuations in GDP. Short-term loans are quite dynamic and largely depend on macroeconomic conditions, provoking market movements. If the relevant direction is supported by medium-term loans, the general market trend will correspond to the GDP trend. The demand for long-term loans is quite inertial, its change does not affect the overall market trend. The constant and variable elements of household demand for credit are highlighted.

Historical national accounting and dating the Great Divergence

AbstractBy offering a particular interpretation of the new evidence on historical national accounting, Goldstone argues for a return to the Pomeranz (2000) version of the Great Divergence, beginning only after 1800. However, he fails to distinguish between two very different patterns of pre-industrial growth: (1) alternating episodes of growing and shrinking without any long-term trend in per capita income and (2) episodes of growing interspersed by per capita incomes remaining on a plateau, so that per capita GDP trends upwards over the long run. The latter dynamic pattern occurred in Britain and Holland from the mid-fourteenth century, so that Northwest Europe first edged ahead of the Yangzi delta region of China in the eighteenth century.

Structural Changes in Economic Growth and Well-Being: The Case of Italy’s Parabola

The controversies on the relationship (called ‘gradient’) between the time trend of GDP and of subjective well-being oppose those who claim that the gradient is positive, to those who argue that it is nil. The possible existence of significant changes of the two trends and of the gradient within the same country is a challenge to both views. By focusing on the case of Italy, we show that the long-run trends of GDP and of well-being turned from increasing to decreasing, and that the gradient exhibits a rise through two structural breaks. This evidence is consistent with the ‘loss aversion’ hypothesis. From this macro-analysis we further go into micro-analysis to explain why subjective well-being declined so steeply. We find that the erosion of trust in others, the increase of financial dissatisfaction, worsened health, and greater importance of income contribute to the decline of subjective well-being. As far as we know, this is the first attempt to test the role of loss aversion in the long run using both macro- and micro-economic approaches.

DO OIL PRICES GOVERN GDP AND PUBLIC SPENDING AVENUES IN SAUDI ARABIA? SENSITIVITY AND TREND ANALYSIS

The oil revenue constitutes 50% of the GDP of Saudi Arabia approximately and indirectly governs the Public spending Avenues (PSA). The study finds out the symmetry in sensitivity and trend of Oil prices (OP), GDP, and Public Spending Avenues (PSA) and the impact of volatility of oil prices on the GDP, and PSA. The analysis is based on the Chain based Index (CBI) and Fixed Based Index (FBI) numbers to get the sensitivity and trend movement of the variables. Coefficient of variations (CV), ANOVA (Analysis of Variance), and correlation computed to get normality, similarity, and the casual relationship of the variables. OP governs the GDP positively and proportionately while there is a negative correlation between the OP variations and the PSA. Saudi Arabian government enhances its spending on PSA and especially on education irrespective of the lowering of OP year by year. OP and GDP do not govern the movement trend of PSA in the long term while shocks of OP and GDP yearly affect the PSA of Saudi Arabia. There is a need to diversify the income resources to minimize the dependency on OP and hedging the budget deficit from the volatility of OP by considering the impact of the sensitivity of OP on the economy and PSA. The policymakers must consider the sensitivity, trend, and governance of GDP and PSA by the OP to formulate the appropriate policies to shift oil economy to non-oil economy to minimize the impact of shocks of OP on the economy.Keywords: Oil prices, Public spending, oil sector revenue, Saudi Arabia, optional resources.JEL Classifications: Q40, Q41, I10, I20, I30DOI: https://doi.org/10.32479/ijeep.10791

Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations

Understanding how biological and environmental factors interactively shape the global distribution of plant and animal genetic diversity is fundamental to biodiversity conservation. Genetic diversity measured in local populations (GDP) is correspondingly assumed representative for population fitness and eco-evolutionary dynamics. For 8356 populations across the globe, we report that plants systematically display much lower GDP than animals, and that life history traits shape GDP patterns both directly (animal longevity and size), and indirectly by mediating core-periphery patterns (animal fecundity and plant dispersal). Particularly in some plant groups, peripheral populations can sustain similar GDP as core populations, emphasizing their potential conservation value. We further find surprisingly weak support for general latitudinal GDP trends. Finally, contemporary rather than past climate contributes to the spatial distribution of GDP, suggesting that contemporary environmental changes affect global patterns of GDP. Our findings generate new perspectives for the conservation of genetic resources at worldwide and taxonomic-wide scales.

An optimal control problem of monetary policy

<p style='text-indent:20px;'>This paper analyses an optimal monetary policy under a non-linear Phillips curve and linear GDP dynamics. A central bank controls the inflation and the GDP trends through the adjustment of the interest rate to prevent shocks and deviations from the long-run optimal targets. The optimal control path for the monetary instrument, the interest rate, is the result of a dynamic minimization problem in a continuous-time fashion. The model allows considering various economic dynamics ranging from hyperinflation to disinflation, sustained growth and recession. The outcomes provide useful monetary policy insights and reveal the dilemma between objectives faced by the monetary authority in trade-off scenarios.</p>

Did Financial Consumers Benefit from the Digital Transformation? An Empirical Investigation and a Case Study

This study aims to document the inter-linkages between two on-going global trends through an empirical investigation – (1) the rapid advancement of the ICT-driven innovations in delivering various types of financial service, and (2) the growing legal and regulatory efforts to protect consumers in the financial markets, after the recent financial crisis in particular. To that end, we compile a set of macro-level indicators on the aggregate outcomes of the financial service sector in Korea during the last three decades, and conduct a regression analysis to gauge the effects of the digital transformation (DT) in the country on those indicators. Using an ARDL-ECM as our empirical model, our results show that: Over time, the unit cost for financial intermediation in the country tends to move in tandem with the growth of economic output, although the profit portion of the cost did not exhibit a long-term relationship with the GDP trend; The long-term effect of the DT trend is negative (i.e., costsaving) for labor input and capital expenditure, which are shown to be statistically significant, and, as a consequence, its impact on the total intermediation cost is also positive and statistically significant. Based on the above outcomes, we elaborate their implications in designing a set of policy instruments to protect financial consumers, and also discuss the Vietnamese case as an illustration to extend this type of macro-level analysis for the purpose of coming up with a policy regime for financial consumers in an emerging-market context.

Analysis and prediction of GDP of three major industries based on grey prediction model

Based on the panel data of China's three major industries' GDP from 2000 to 2020, this paper studies the development trend and trend of China's GDP in the past 20 years from the perspective of data analysis, finds that China's GDP industrial structure has changed from "one, two and three" pattern to "three, two and one" pattern, and gives the personal prediction value of the three major industries' GDP in the next few years by using the grey GM (1,1) model, Through the test of the model, the reliability of the result is high. In addition, it gives its own outlook and views on the impact of relevant policies.

Regime-dependent effects of selected macroeconomic variables on CO<SUB align="right">2 emissions: evidence from China

The aim of this study is to examine the nonlinear effects of the macroeconomic variables on CO2 emission level and to determine how the cyclical patterns of carbon emission growth change along the changes in macroeconomic variables' growth rate under a regime switching mechanism in China employing nonlinear Granger causality and Markov-switching dynamic regression models. The results show no bidirectional causality between carbon emission level and the macroeconomic variables used in the model. The cyclical relationship between carbon emission level and income was negative in the high emission regime, but it became positive once a low-emission growth regime was reached. Specifically, the negative sign on GDP growth under the high-emission growth regime and the positive sign on GDP growth under low-emission growth regime and, furthermore, the changing cyclical behaviour of emissions growth with the continuous upward trend of GDP is compatible with the existence of an N-shaped EKC in the case of China.

The impact of COVID – 19 on Collecting of Tax Revenues in Kosovo

This paper aims to present the effect of the pandemic crisis COVID - 19 on the economy of Kosovo, namely tax revenues focusing on the impact of the pandemic situation on macroeconomic indicators such as Gross Domestic Product. The impact of the crisis on the collection of tax revenues, the impact of the crisis on tax evasion, the effect of the crisis on business turnover, and the ratio of tax revenues to GDP. The data that have been used in this paper are data from primary sources through questionnaires and data from secondary sources. The findings of this research show that the pandemic situation in 2020 has slowed down local economic growth, declined trend of tax revenues, declined trend of GDP compared to the previous periods, and other negative effects such as declined economic activity, business, and employment level.

Empirical Study on the relationship between freight volume and economic development of provinces and cities in China

The transportation industry is the basic and leading industry of the national economy, and it is an important pillar of the national economy. The freight transportation is an important part of the transportation and plays an important role in the economic development. At the same time, the development of freight transport itself is also affected by the economy. Therefore, this paper studies the relationship between cargo transportation and economic growth, and explores the internal relationship and law between them, so as to provide reference for the development of cargo transportation and the growth of national economy. This paper selects the freight volume and GDP indicators of 31 provinces and cities in China from 1993 to 2018, and discusses the development trend of GDP and freight volume from the perspective of provinces and cities. The results are as follows: 1. Freight volume and GDP showed an upward trend from 1993 to 2018, but the growth of freight volume was slower than that of GDP. 2. There is a co integration relationship between GDP and freight volume, and the national GDP increases by 0.954% for every 1% increase in freight turnover. 3. There are differences in the driving effect of freight volume in typical regions and provinces.

Analysis of Okun’s Regularity: The Case of Bosnia and Herzegovina

Abstract Economic growth, or, more precisely, GDP growth rate (Y) is the key macroeconomic indicator. Positive tendencies of GDP growth (r) mostly impact the economic welfare of the national economy. Hence, output is analysed in combination with other macroeconomic aggregates and indicators: investments, inflation, unemployment, trade exchange and the like. In the context of researching the relation between GDP trends and unemployment rate, A.M. Okun was the first to notice regularity between these variables. Okun’s law shows the direction and strength of GDP changes under the influence of (un)employment and represents functional relation between the output market and labour market. This paper aims to prove that, to a certain extent, there exists Okun’s regularity in Bosnian and Herzegovinian economy for the period of 2000-2019. The results of linear regression analysis show that there is negative correlation (R=0,451) between variables Y (output) and X (unemployment). Determination coefficient R2 = 0,204 indicates that 20,4% variations in GDP is determined by the changes in unemployment. Coefficient β in linear regression equation is negative, amounting to -0,596, which means that a change in unemployment by 1 percentile point brings about an inverse change in GDP by approximately 0,6 units. Variable direction indicates that there exists Okun’s regularity, though with significantly weaker intensity among the variables. This proves the hypothesis that there is a negative functional relation between the output market and labour market in Bosnia and Herzegovina.

Cause of death variation under the shared socioeconomic pathways.

Climate change will create numerous risks for human health, including impacts associated with temperature extremes, diarrheal diseases, and undernutrition. Such risks, along with other socioeconomic and development trends, will affect cause-of-death patterns experienced in the coming decades. This study explores future mortality trends using the shared socioeconomic pathways (SSP) framework, a widely utilized tool for understanding socioeconomic development trends in a world with climate change. Existing projections for GDP, urbanization, and demographic trends based on SSP narratives are incorporated into an integrated assessment model, International Futures (IFs), in order to project mortality levels by cause of death for all countries from 2020 to 2100. Under more optimistic SSPs, non-communicable diseases (NCDs) rise as a proportion of all deaths, particularly in low- and middle-income countries, while more pessimistic SSPs suggest a continued high burden of largely preventable communicable diseases. In high-income countries, significant continued burdens of NCDs are projected for the remainder of the century under all SSPs. Comparisons are also made to recent cause-of-death projections from the Institute for Health Metrics and Evaluation (IHME) to assess how the IFs and IHME models vary.Electronic supplementary materialThe online version of this article (10.1007/s10584-020-02824-0) contains supplementary material, which is available to authorized users.

Application of a novel grey forecasting model with time power term to predict China's GDP

PurposeGross domestic product (GDP) is an important indicator to measure a country's economic development. If the future development trend of a country's GDP can be accurately predicted, it will have a positive effect on the formulation and implementation of the country's future economic development policies. In order to explore the future development trend of China's GDP, the purpose of this paper is to establish a new grey forecasting model with time power term to forecast GDP.Design/methodology/approachFirstly, the shortcomings of the traditional grey prediction model with time power term are found out through analysis, and then the generalized grey prediction model with time power term is established (abbreviated as PTGM (1,1, α) model). Secondly, the PTGM (1,1, α) model is improved by linear interpolation method, and the optimized PTGM (1,1, α) model is established (abbreviated as OPTGM (1,1, α) model), and the parameters of the OPTGM (1,1, α) model are solved by the quantum genetic algorithm. Thirdly, the advantage of the OPTGM (1,1, α) model over the traditional grey models is illustrated by two real cases. Finally the OPTGM (1,1, α) model is used to predict China's GDP from 2020 to 2029.FindingsThe OPTGM (1,1, α) model is more suitable for predicting China's GDP than other grey prediction models.Originality/valueA new grey prediction model with time power term is proposed.