Is it Abenomics or Post-Disaster Recovery? A Counterfactual Analysis

In the United States, income and expenditure-side estimates of gross domestic product (GDP) (GDP I and GDP E ) measure “true” GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDP E , GDP I , unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error perspective (i.e., the two GDP proxies are assumed to equal true GDP plus measurement error). Without further restrictions, our model is unidentified. We consider a range of restrictions that allow for point and set identification of true GDP and show that they lead to informative monthly GDP estimates. We illustrate how these new monthly data contribute to our historical understanding of business cycles and we provide a real-time application nowcasting monthly GDP over the pandemic recession.

Canadian and U.S. Real Income Growth Pre and Post 2000: A Reversal of Fortunes

Before we can understand the economy, we need to know how to describe and measure it. In this chapter we focus on gross domestic product (GDP), the most central quantity in macroeconomics. What is it? How is it created? How do we measure it? What is its relationship to other important quantities? How does it change over time?

Ghost of GDP: Sponge, Myth or Moustache Twitching?

PurposeWorld over, change of base year in the gross domestic product (GDP) is a standard practice of GDP estimation. However, unless a consistent series of GDP is released with respect to the new base for the earlier period, the existence of multiple growth rates creates problems for applied researchers, policymakers and the general public alike. Faced with such a menu of GDP series researchers often try to interpolate a consistent series of GDP. The main purpose of this paper is to analyses the nature of the data generating process of such multiple interpolated series of quarterly growth rates and tries to discern the consistency of such processes.Design/methodology/approachThe present paper tries to look into the statistical implications and complications of such interpolated quarterly GDP/growth series in India in terms of three series of GDP, namely, with 1999–2000, 2004–2005 and 2011–2012 as its bases.FindingsThe analysis reveals that as a result of a change of base year, the nature of the data generating process of the old and new GDP series could undergo changes and experience different breakpoints. While all these conclusions seem to be valid for GDP growth at quarterly intervals, taking the data at annual frequency is less problematic.Practical implicationsThe observation suggests that in most applied work, researchers may not have the luxury of only working with annual data and certain consistency checks will be necessary to check the veracity of the results based on quarterly data with those based on annual data. Second, moving forward it may be useful for the Authorities to make a transition to a chain-based linking method rather than fixed time-period-based bases as is currently done.Originality/valueThe analysis of Indian GDP in this paper is, perhaps, indicative of the fact that usage of quarterly GDP data is to be handled with caution and it is preferable that any serious empirical analysis uses annual GDP data whenever it is available/feasible. The comparison of GDP growth rates at different frequencies and examining the true nature of the process are quite unique in their contribution towards empirical macroeconomic research.

The ongoing COVID-19 pandemic process we all face currently has highlighted the fact that digitalization of economic activities is very crucial. Digital economy became even more important after the pandemic. When the governments allocate a greater proportion of Gross Domestic Product on some public services, the lower the proportion spent on other goods and services. For this reason, many countries want to estimate the Gross Domestic Product spent on public services within the scope of public control and risk management. The main goal in this study is to predict Gross Domestic Product per capita based on digital indicators such as fixed-broadband subscriptions, mobile-cellular telephone penetration rate, fixed-telephone subscriptions, internet penetration rate, research and development expenditure and patent applications. Artificial Neural Networks are employed to predict Gross Domestic Product per capita via digital indicators. Artificial Neural Network method is used to estimate the digital economy for 29 OECD countries between 2002–2018. The results demonstrate that Artificial Neural Networks can be utilized effectively in applications of digital economy forecasting. According to the results, coefficient of determination (R2 = 0.92) is very high, meaning that Artificial Neural Networks can be effectively utilized in Gross Domestic Product per capita estimations through digital indicators. This article differs from previous studies as it uses digital economic indicators in predicting Gross Domestic Product per capita by applying Artificial Neural Networks model.

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.

Nigeria is the most populous country in Africa, it maintains the second largest oil reserve in the continent. The question on whether the oil natural resource endowment is a blessing or a curse is still not adequately answered in the literature. This study therefore examines the interrelationships between petroleum revenue and gross domestic product in Nigeria from 1985-2020. The data used for the analysis of the study was obtained from World Bank and Central Bank of Nigeria data base. Augmented Dickey Fuller (ADF) unit root test was used to test for the presence of stationarity in the variables, autoregressive distributed Lag (ARDL) co-integration test was employed to test for long run relationship and error correction model (ECM) was employed to test the short-run relationship among variables. The variables used for the research work are Gross Domestic Product (GDP), Oil Revenue (OIR), Inflation (INF), Effective Exchange Rate (EEXR), Labour (LAB) and Gross Capital Formation (GCFC). The study showed that oil revenue and labour force exhibit significant positive impacts on gross domestic product (economic growth). This study recommends that government should establish annual spending limits in order to reduce inflation, the government should engage in the rehabilitation of the old refineries and construction of new ones in order to stop the age long practice of exporting crude oil at a low price and purchasing the refined oil at a high price. Measures to reduce the high exchange rate of naira with other countries’ currencies should be put in place. Various government policies should be put in place and diligently enforced to ensure that the diversion of revenue gotten from the oil sector are duly handled to further meet the economic growth objective in the country.

Estimates of gross domestic product (GDP) play a significant role in evaluating the economic performance of a country or region. Understanding the spatiotemporal process of GDP growth is important for estimating or monitoring the economic state of a region. Various GDP studies have been reported, and several studies have focused on spatiotemporal GDP variations. This study presents a map spectrum-based clustering approach to analyze the spatiotemporal variation patterns of GDP growth. First, a sequence of nighttime light images (from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS)) is used to support the spatial distribution of statistical GDP data. Subsequently, the time spectrum of each spatial unit is generated using a time series of dasymetric GDP maps, and then the spatial units with similar time spectra are clustered into one class. Each category has a similar spatiotemporal GDP variation pattern. Finally, the proposed approach is applied to analyze the spatiotemporal patterns of GDP growth in the Wuhan urban agglomeration. The experimental results illustrated regional discrepancies of GDP growth existed in the study area.

Greenhouse Gases and Human Well-Being: China in a Global Perspective

WHO African region has got the highest maternal mortality rate compared to the other five regions. Maternal mortality is hypothesized to have significantly negative effect on the gross domestic product (GDP). The objective of the current study was to estimate the loss in GDP attributable to maternal mortality in the WHO African Region. The burden of maternal mortality on GDP was estimated using a double-log econometric model. The analysis is based on cross-sectional data for 45 of the 46 Member States in the WHO African Region. Data were obtained from UNDP and the World Bank publications. All the explanatory variables included in the double-log model were found to have statistically significant effect on per capita gross domestic product (GDP) at 5% level in a t-distribution test. The coefficients for land (D), capital (K), educational enrolment (EN) and exports (X) had a positive sign; while labor (L), imports (M) and maternal mortality rate (MMR) were found to impact negatively on GDP. Maternal mortality of a single person was found to reduce per capita GDP by US$ 0.36 per year. The study has demonstrated that maternal mortality has a statistically significant negative effect on GDP. Thus, as policy-makers strive to increase GDP through land reform programs, capital investments, export promotion and increase in educational enrolment, they should always remember that investments in maternal mortality-reducing interventions promises significant economic returns.African Journal of Health Sciences Vol. 12(3-4) 2005: 55-64

As an emerging research area, application of satellite-based nighttime lights data in the social sciences has increased rapidly in recent years. This study, building on the recent surge in the use of satellite-based lights data, explores whether information provided by such data can be used to address attenuation bias in the estimated coefficient when the regressor variable, Gross Domestic Product (GDP), is measured with large error. Using an example of a study on infant mortality rates (IMRs) in the People’s Republic of China (PRC), this paper compares four models with different indicators of GDP as the regressor of IMR: (1) observed GDP alone, (2) lights variable as a substitute, (3) a synthetic measure based on weighted observed GDP and lights, and (4) GDP with lights as an instrumental variable. The results show that the inclusion of nighttime lights can reduce the bias in coefficient estimates compared with the model using observed GDP. Among the three approaches discussed, the instrumental-variable approach proves to be the best approach in correcting the bias caused by GDP measurement error and estimates the effect of GDP much higher than do the models using observed GDP. The study concludes that beyond the topic of this study, nighttime lights data have great potential to be used in other sociological research areas facing estimation bias problems due to measurement errors in economic indicators. The potential is especially great for those focusing on developing regions or small areas lacking high-quality measures of economic and demographic variables.

Based on the Kuznets's environmental theory, we examine the effect on carbon dioxide (CO2) emission of gross domestic product (GDP), energy consumption, trade openness, and industrial structure in OPEC countries during 1974-2013, using the Full Modified Ordinary Least Square (FMOLS) method. We find that GDP, energy consumption, and industrial structure have positive effects on CO2 emission. The GDP squared, however, negatively affects CO2, although the effect is small. The positive effect of GDP but negative effect of GDP squared validates the environmental Kuznets curve for OPEC countries. In addition, we find that an increase in trade openness leads to decrease in CO2 emission. Using the panel vector error correction model (VECM) and Granger causality test, we confirm that long-run causality is supported for all of our equations. We also find bi-directional short-run causality between energy consumption and CO2 emission and between energy consumption and industrial structure. Moreover, we find one-way causality running from energy consumption to GDP, from CO2 emission to trade openness, from GDP to trade openness, and from GDP to emission.

Assessing the economic impact of public investment in Malaysia: a case study on MyRapid Transit project using a dynamic computable general equilibrium model

This study forecasts standard of living in Tanzania over the next ten years as measured by the ratio of two rates: (1) economic growth, using real gross domestic product (GDP), to (2) population growth. GDP alone is an insufficient measure of a nation’s well-being. China and India, for example, have high levels of GDP, but the pie is sliced very thin to be shared among the billions of people who live in those two countries. Real GDP per capita measures not only the level of economic activity but also the number of people who must share in the results of that activity. This study uses historical data for Tanzania’s real GDP (i.e., adjusted for inflation) and population to observe the ratio over time (from 1960 to 2011). One discovery is that of a point of inflection at the end of the 20th century, such that GDP per capita rose at an annual rate of just one percent “pre-inflection” and four times that rate “post-inflection.” The study uses standard statistical methods to forecast the variables over the next ten years. Results of the forecast indicate that GDP will continue to grow at a faster rate than will population, with a consequent steady rise in the average standard of living. How the increased prosperity will be distributed, so as to reduce the level of poverty and the disparity of income, remains to be seen.