Showing papers on "Real gross domestic product published in 2022"

Why Has the U.S. Economy Stagnated since the Great Recession?

Abstract: Abstract Since the Great Recession in 2007–2009, U.S. real GDP has failed to return to its previously projected path, a phenomenon widely associated with secular stagnation. We investigate whether this stagnation was due to hysteresis effects from the Great Recession, a persistent negative output gap following the recession, or slower trend growth for other reasons. To do so, we develop a new Markov-switching time series model of output growth that accommodates two different types of recessions: those that permanently alter the level of real GDP and those with only temporary effects. We also account for structural change in trend growth. Estimates from our model suggest that the Great Recession generated a large, persistent negative output gap rather than any substantial hysteresis effects, with the economy eventually recovering to a lower trend path that appears to be due to a reduction in productivity growth that began prior to the onset of the Great Recession.

Comparative Analysis of Trade Liberalization, CO2 Emissions, Energy Consumption and Economic Growth in Southeast Asian and Latin American Regions: A Structural Equation Modeling Approach

Abstract: This study compares the nexus among trade liberalization, CO2 emissions, energy consumption, and economic growth in Southeast Asian and Latin American countries. We apply the structural equation modeling approach for estimation analysis of the data from 1991 to 2018. The empirical findings of this study validate that trade has a positive and statistically significant effect on energy consumption, CO2 emissions, and gross domestic product (GDP) in Southeast Asian countries. Whereas in Latin American countries, trade shows a positive insignificant impact on energy consumption, but the coefficients for both CO2 emissions and GDP are positive and statistically significant. Energy consumption also exhibits a positive significant effect on CO2 emissions and a positive statistically insignificant effect on GDP in the Southeast Asian region. However, in Latin American countries, energy consumption predicts a positive and statistically significant impact on both CO2 emissions and GDP. Whereas, CO2 emissions indicate a positive significant effect on GDP in both regions. Therefore, each country’s government in both areas should formulate appropriate policies to promote green technologies in the production and exports, which could help economies to achieve a clean environment and sustainable long-term development.

Saudi Non-Oil Exports before and after COVID-19: Historical Impacts of Determinants and Scenario Analysis

Abstract: The diversification of the economy including its exports is at the core of Saudi Vision 2030. The vision targets to raise non-oil export from 16% to 50% of non-oil GDP by 2030. Achieving this, in addition to other goals, necessitates a better understanding of the non-oil export relationship with its determinants. However, we are not aware of a study that estimates the impacts of the determinants on Saudi non-oil exports covering the recent years of reforms and low oil prices and that conducts simulations for future. The purpose of this study is to develop an econometric modeling framework for Saudi non-oil export that can enhance informing the policymaking process through empirical estimations and simulations. For estimations, we applied cointegration and equilibrium correction methodology to the annual data for the period 1983–2018. Results show that Middle Eastern and North African countries’ GDP, as a measure of foreign income, and Saudi Arabia’s non-oil GDP, as a measure of production capacity, have statistically significant positive effects on Saudi non-oil exports in the long run. The real effective exchange rate (REER), as a measure of competitiveness, also exerts a positive effect in the long run if it depreciates and vice versa. Furthermore, our findings support the Export-led growth concept, which articulates that export can be an engine of economic growth and does not support the Dutch disease concept, which highlights the consequences of the resource sector for the non-resource tradable sector for Saudi Arabia. Macroeconometric model-based simulations conducted up to 2030 reveal out that the Saudi non-oil export is more responsive to the changes in REER than any other determinants. The simulation results also show that non-oil manufacturing makes a three times larger contribution to the future expansion of non-oil exports than agriculture. Moreover, the simulations discover that finance, insurance, and other business services, as well as transport and communication play an important role in improving the Saudi non-oil export performance in the coming decade. The key policy recommendation is that measures should be implemented in a coordinated and balanced way to achieve non-oil exports and other targets of the Vision.

Impact of fossil fuels and renewable energy consumption on economic growth in Paris Club Countries

Abstract: The relationship between economic growth (in terms of GDP) and renewable energy (RE) and nonrenewable energy (NRE) consumption was investigated in 20 countries featured on the Renewable Energy Country Attractiveness Index list, also known as the Paris Club. The effect of both RE and NRE consumption on economic growth is discussed in the growth model based on the neoclassical production function. Labor and capital, which are important dynamics of growth, are also considered in the model. Granger causality and panel vector autoregression analysis are performed for the period 1991–2016. The results show that neither RE nor NRE consumption has a positive effect on economic growth. In reality, a 1% increase in RE consumption will reduce the GDP growth by 0.14%. For the effect of GDP growth on energy types, if growth increases by 1%, NRE consumption increases by 5.54%. If economic growth increases by 1%, a reduction of 1.73% occurs in RE consumption. In contrast, a causal link between both types of energy to growth has not been determined. There is no statistically significant coefficient of NRE and capital factors on GDP. A mutually positive and statistically significant relationship was determined between labor and growth. According to the results of variance decomposition, the basic dynamic of growth is itself: over a ten-year period, growth was affected by itself by 98%.

Nowcasting world GDP growth with high‐frequency data

Abstract: Although the Covid-19 crisis has shown how high-frequency data can help track the economy in real time, we investigate whether it can improve the nowcasting accuracy of world GDP growth. To this end, we build a large dataset of 718 monthly and 255 weekly series. Our approach builds on a Factor-Augmented MIxed DAta Sampling (FA-MIDAS), which we extend with a preselection of variables. We find that this preselection markedly enhances performances. This approach also outperforms a LASSO-MIDAS—another technique for dimension reduction in a mixed-frequency setting. Though we find that a FA-MIDAS with weekly data outperform other models relying on monthly or quarterly data, we also point to asymmetries. Models with weekly data have indeed performances similar to other models during “normal” times but can strongly outperform them during “crisis” episodes, above all the Covid-19 period. Finally, we build a nowcasting model for world GDP annual growth incorporating weekly data that give timely (one per week) and accurate forecasts (close to IMF and OECD projections but with 1- to 3-month lead). Policy-wise, this can provide an alternative benchmark for world GDP growth during crisis episodes when sudden swings in the economy make usual benchmark projections (IMF's or OECD's) quickly outdated.

The effect of exchange rates, exports and imports on economic growth in indonesia

Abstract: The country's economic growth can be seen from the price of gross domestic product (GDP). Gross domestic product can be used as one of the benchmarks in fostering economic improvement from various sectors indirectly. Economic growth is stimulated through various factors, including import prices as well as changes in the price of the rupiah or the exchange rate of the rupiah. This study aims to determine the impact of exports and imports on financial improvement in Indonesia. This research technique uses a quantitative approach. The records received in this observation are secondary records received from the World Bank in the form of time collection from 1989 to 2018. Data evaluation is carried out through more than one regression evaluation with the help of time series tools. The results of the study reveals that the exchange rate and import variables have a major effect on economic growth. Meanwhile, the export variable has a negative impact on economic growth.

The ICT, financial development, energy consumption and economic growth nexus in MENA countries: dynamic panel CS-ARDL evidence

Abstract: ABSTRACT This paper analyses the nexus between renewable and non-renewable energy consumption, financial development, Information and Communication Technology (ICT) diffusion and economic growth, in MENA countries, over the period 1980–2018. We use the novel Cross-Section augmented Autoregressive Distributed Lag (CS-ARDL) estimation technique which accounts for cross-sectional dependence and cross-country heterogeneity issues. We find a positive impact of renewable and non-renewable energy on economic growth, but a negative effect of financial development on economic growth. We also find a positive and statistically significant influence of ICT on Gross Domestic Product (GDP). Renewable energy and ICT diffusion can be considered important determinants of improved economic activity, job creation and better environmental quality. Pairwise Dumitrescu-Hurlin panel causality tests were used to examine the causal relations among the variables. The findings of this study have considerable policy implications for the selected countries.

Nightlights and Subnational Economic Activity: Estimating Departmental GDP in Paraguay

Abstract: Subnational measures of economic activity are crucial for analyzing inequalities that persist across subnational regions and for tracking progress towards sustainable development within a country. Eighteen of the Sustainable Development Goals (SDG) indicators require having estimates of Gross Domestic Product (GDP), making subnational GDP estimates crucial for local SDG monitoring. However, many countries do not produce official subnational GDP estimates. Using Paraguay as an example, we show how nightlights imagery from the Visible Infrared Imaging Radiometer Suite’s Day-Night Band (VIIRS-DNB) and data from neighboring countries can be used to produce subnational GDP estimates. We first estimate the relationship between VIIRS and economic activity in South American countries at the first subnational administrative level, employing various econometric models. Results suggest that nightlights are strongly predictive of subnational GDP variation in South American countries with available data. We assess various models’ goodness-of-fit using both cross-validation against other countries’ subnational GDP data and comparing predictions against an input–output accounting of Paraguay’s subnational GDP. Finally, we use the preferred model to produce a time series of department-level GDP in Paraguay.

Human capital and economic growth nexus in Nigeria

Abstract: The importance of human capital to economic growth and development cannot be overstressed, particularly for the fact that human capital is central to the development process of any economy. Therefore, this study examines the impact of human capital development on economic growth in Nigeria from 1981 – 2018. The study used the Ordinary Least Square (OLS) technique, and it focuses on the impact of government expenditure on education, health, and economic growth and the direction of causality between the human capital variables and economic growth in Nigeria. The findings show that government expenditure on education statistically and significantly affects real GDP. However, government expenditure on health had a positive and insignificant impact on real GDP. It was also found that gross fixed capital formation and population growth positively and statistically significantly affects real GDP and foreign direct investment had a negative and insignificant impact on real GDP. Also, a significant unidirectional causality was found running from government expenditures on education and health to real GDP. The study recommends, therefore, that expenditure on education be sustained and increase healthcare expenditure. These recommendations, no doubt, would bring about qualitative human capital that would further enhance economic growth and development in Nigeria.

How Far Are We from the Planetary Health Diet? A Threshold Regression Analysis of Global Diets

Abstract: Global diets and food system not only influence human health conditions but also have a great effect on environmental sustainability. The Planetary Health Diet (PHD) proposed by the Lancet Commission is considered as a sustainable diet that meets human’s nutritional demands yet poses less pressure on the environment. In this study, we examine how the economic condition, i.e., Gross Domestic Product per capita (GDP per capita), affects the deviations of current diets from the PHD at the country level by using a threshold regression model. The results show three dimensions regarding food consumption patterns in all 11 kinds of foods across the globe, as evidenced from the data in 147 countries as of 2018. First, the findings indicate that there exist deviations from the PHD for all kinds of foods, which could guide policymakers to make dietary improvements. Second, we find that GDP per capita impacts food consumption patterns with all kinds of foods. The results demonstrate that the changing rates of food consumption amounts decrease as the GDP per capita increases. Finally, we calculate the GDP per capita thresholds for all kinds of foods, and we find the number of thresholds ranging from zero to two. Specifically, 20,000 PPP (current international $), the GDP per capita boundary distinguishing developing and developed countries, is the first GDP per capita threshold influencing the food consumption amount. What is more, the second GDP threshold is 40,000 PPP (current international $), which is the average GDP per capita of developed countries. Thus, we identify the countries that require more financial assistance from a GDP per capita perspective.

Reconciled Estimates of Monthly GDP in the US*

Abstract: In the US, income and expenditure side estimates of GDP (GDPI and GDPE) measure “true” GDP with error and are available at the quarterly frequency. Methods exist for producing reconciled quarterly estimates of GDP based on GDPI and GDPE. In this paper, we extend these methods to provide reconciled historical GDP estimates at the monthly frequency from 1960. We do this using a Bayesian Mixed Frequency Vector Autoregression involving GDPE, GDPI, unobserved true GDP and monthly indicators of short-term economic activity. We illustrate how the new monthly data contribute to our historical understanding of business cycles.

The asymmetric effects of real variables on real housing prices: a nonlinear ARDL analysis for Turkey

Abstract: Purpose This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices. Design/methodology/approach This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10. Findings The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices. Originality/value This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

The Ottoman Economy (1870-1913): Preliminary Second-Generation Estimates

Abstract: The purpose of this study is to present a new estimate on the GDP and per capita GDP levels of the Ottoman Empire between 1870 and 1913. Since the earlier estimates employ different methods, this study re-combines data series using reliable secondary sources while attempting to incorporate the distribution margins, includes industrial production excluded from the industrial census, and correcting missing information in certain agricultural productions. The inclusion of “Distribution Margins” (DMs) that adjust from producer to market prices reflects the price difference which entails diverse GDP and per capita GDP levels. The DMs are crucial, especially to incorporating costs of distributing industrial and agricultural products between the center and periphery. This research concludes that GDP and per capita GDP levels were higher than those made by earlier estimates. The method and findings of this study make contributions to the recent discussions on the economic performance of the Ottoman Empire, particularly for the period preceding the First World War. This study also suggests new research areas to further improve future studies on GDP and per capita GDP levels of the Ottoman Empire.

Predicting gross domestic product to macroeconomic indicators

Abstract: Macroeconomic indicators enable countries to concentrate on goods, services, and other entities that grow their Gross Domestic Product (GDP). Often, identifying these groups of indicators poses a challenge to nations. The study considered a typical data set with two main objectives. First, to predict GDP to macroeconomic indicators by applying four machine learning methods namely, Principal Component Regression (PCR), Ridge Regression (RR), Lasso Regression (LR), and Ordinary Least Squares (OLS). Second, identify the most likely key macroeconomic variables that could affect the growth of GDP. The methods were evaluated using 5-fold cross-validation, and the estimated coefficients associated with the macroeconomic indicators were computed. The results revealed that PCR method with an accuracy of 89% and a mean square error of -7.552007365635066e+21 predicted GDP to macroeconomic indicators accurately, more than other methods. Some macroeconomic indicators did affect GDP positively, while others did not. The major contribution of the study is the use of machine learning regularization methods to predict GDP instead of the traditional statistical methods. It also identified additional macroeconomic variables to compute real GDP.

Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic

Abstract: Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China’s major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China’s major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R2 models generated by the total value of NTL and the GDP of secondary and tertiary industries being 0.83 and 0.93. (2) The impact of the COVID-19 pandemic on the GDP of cities with varied degrees of development and industrial structures obviously varies across time and space. The GDP of economically developed cities such as Beijing and Shanghai are more affected by COVID-19, while the GDP of less developed cities such as Xining and Lanzhou are less affected by COVID-19. The GDP of China’s major cities fell significantly in February. As the COVID-19 outbreak was gradually brought under control in March, different cities achieved different levels of GDP recovery. This study establishes a fine spatial and temporal distribution estimation model of urban GDP by industry; it accurately monitors and assesses the spatial and temporal distribution characteristics of urban GDP during the COVID-19 pandemic, reveals the impact mechanism of the COVID-19 pandemic on the economic development of major Chinese cities. Moreover, economically developed cities should pay more attention to the spread of the COVID-19 pandemic. It should do well in pandemic prevention and control in airports and stations with large traffic flow. At the same time, after the COVID-19 pandemic is brought under control, they should speed up the resumption of work and production to achieve economic recovery. This study provides scientific references for COVID-19 pandemic prevention and control measures, as well as for the formulation of urban economic development policies.

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

Abstract: GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a result, the GDP trend forecast partially reflects China’s transformation and future development. The time series ARIMA (Autoregressive Integrated Moving Average) model and the BPNN (BP neural network) model are combined in this article to create the ARIMA-BPNN fusion prediction model. The predicted values of the two models were then weighted averaged to obtain the predicted values of the linear part of the improved fusion model. To get the predicted values of the improved fusion model, we weighted average the residual parts of the two models, predict the nonlinear residual with BPNN, and add the predicted values of the two parts. It is applied to the actual GDP forecast in H province from 2019 to 2022, and the actual forecast verifies the effectiveness of the fusion forecast model in the actual forecast.