Estimates Of Gross Domestic Product Research Articles

Experience in Measuring Estimates of Russian GDP Dynamics Using Double Deflation Approach

When constructing estimates of gross domestic product (GDP) and gross value added (GVA) at constant prices, three approaches or a combination of them are used: extrapolation, simple deflation and double deflation. The latter approach is considered preferable, but is not yet used in Russian statistics. The article is devoted to the issues of constructing estimates of GDP and GVA dynamics for the Russian Federation using double deflation. The interest in constructing such estimates is due to the possibility of taking into account the dependence of production on domestic and import supplies, which the other two approaches lack. Received for the period from 2011 to 2016 estimates were compared with official indicators for the overall economy, the service sector, and for mining and manufacturing.From 2011 to 2016 alternative estimates of the physical volume indices of GDP and GVA showed lower growth rates compared to official indicators. This is due to the faster growth of alternative estimates of intermediate consumption compared to official ones. In the structure of intermediate consumption, domestic intermediate products were replacing imported analogues. The current dynamics of indicators, to a first approximation, shows signs of localization of production, which may be associated with the transition of enterprises to domestic outsourcing and the implementation of an import substitution policy.At the same time, the presence of measurement problems in the use of double deflation is obvious, most clearly manifested when moving to the analysis of indicators of lower levels of aggregation. The analysis shows in favor of conducting additional research related to the introduction of the double deflation technique in the official methodology.

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.

Benchmarking Latvia’s economy: a new estimate of gross domestic product in the 1930s

Benchmarking Latvia’s economy: a new estimate of gross domestic product in the 1930s

Effective GDP: A Cross‐Country Comparison

AbstractA large literature has developed around alternatives to gross domestic product (GDP) per capita for cross‐country comparisons of well‐being. Many of these studies point to non‐economic factors that affect well‐being but are not captured in the traditional GDP measure. With a comprehensive coverage of economies available through the World Bank's International Comparison Programme (ICP) databases at six‐yearly intervals, this paper utilises the ICP 2011 Purchasing Power Parity (PPP)‐based expenditures and builds upon it through an analysis of residuals from cross‐country Engel curve regressions. The methodology draws on the idea that patterns in Engel curve residuals can reveal inter alia previously unmeasured aspects of human well‐being such as optimism or pessimism. The paper demonstrates how a residual‐based model of Revealed Optimism/Pessimism can be neatly integrated with PPP‐based GDP to provide an extended measure of national well‐being, referred to here as ‘Effective GDP’. As a result, a wider disparity in well‐being across economies is revealed than could be discerned simply from the ICP data. The results suggest that, in 2011, Effective GDP extends traditional PPP‐based GDP estimates by around two per cent in the positive direction for apparently optimistic economies and as much as five per cent downwards for pessimistic ones.

Dynamic monitoring and modeling of the growth-poverty-inequality trilemma in the Nile River Basin with consistent night-time data (2000–2020)

• A novel technical framework was proposed for dynamical economic development monitoring and the GPI trilemma modeling with NTL RS approach. • A new NTL-based GDP estimation and spatialization strategy was developed for regions without abundant field surveying data. • The GPI trilemma in the Nile River basin was firstly modeled combined with NTL RS data and statistical data. • The inter-relationships between each of the economic growth, poverty and inequality were discussed. Aligning with UN Sustainable Development Goals (SDGs) 1 and 10, the ‘growth-poverty-inequality’ (GPI) nexus has become widely discussed in economic development research. It remains an important discourse regarding sustainable development. To monitor spatiotemporal economic development and analyse the GPI trilemma, especially in developing and undeveloped regions, a novel framework based on long-term, consistent night-time light (NTL) remote sensing (RS) data (2000–2020) was proposed and applied to the Nile River Basin. An optimized, multitemporal estimation strategy was developed for gross domestic product (GDP) estimation and spatialization. Then, the poverty map and inequality indices for the entire basin and each country were prepared. Combining GDP growth, the poverty map, inequality index, and other relevant economic statistics, the GPI nexus for the Nile River Basin was modelled. Findings suggest that the proposed framework can not only effectively map GPI dynamics but also accurately model the GPI nexus. We found that the economic development of countries in the Nile River Basin is characterised by widespread poverty and inequality and restricted spatial distribution. The spatiotemporal evolution patterns of GPI vary with upstream and downstream geographic locations. Regarding their interaction, study findings revealed that economic growth relieves poverty, whereas high inequality levels aggravate poverty. Moreover, high inequality dampens the positive effect of economic growth on poverty reduction. This study offers new insight into GPI trilemma modeling and analyses using NTL data, an economically viable alternative to mass field surveys that is especially relevant for developing and underdeveloped regions.

Reconciled Estimates of Monthly GDP in the United States

In the United States, income and expenditure-side estimates of gross domestic product (GDP) (GDP and GDP) 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, GDP, 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.

Nightlights and Subnational Economic Activity: Estimating Departmental GDP in Paraguay

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.

Evaluating the performance of LBSM data to estimate the gross domestic product of China at multiple scales: A comparison with NPP-VIIRS nighttime light data

Regional economic development evaluation is essential for understanding social and environmental issues. Although the nighttime light (NTL) data have been proved to be effective in economic estimation, it cannot reflect the human activities that occur during the daytime. Recently, with the widespread use of smart mobile devices, the location based social media (LBSM) data are increasingly being used as a proxy for real-time human activities. However, little work was carried out to explore the potential of LBSM data in estimating economic development at different scales in China. This study filled this gap by evaluating the effectiveness of Tencent user density (TUD) data, a typical type of LBSM data in China, in Gross Domestic Product (GDP) modeling at the provincial, municipal, and county scales. In this study, we employed holiday and non-holiday TUD sample data to simulate the annual TUD data, and compared it with the new generation NTL data, NPP/VIIRS images. The results showed that although the simulated annual TUD data does not perform better than NPP/VIIRS-NTL data in provincial and municipal GDP estimation, it outperforms NPP/VIIRS-NTL data at the county scale. More importantly, the simulated annual TUD data are much more powerful and reliable than NPP/VIIRS-NTL data in underdeveloped areas with complex terrain, such as the Northwest and Southwest China, as well as in more developed areas with separation of work and housing, such as the North China and South China. This is mainly because TUD data can reduce the impact of natural factors such as terrain on data collection as well as reflect both daytime and nighttime human activities. This study confirmed that the LBSM-TUD data is a potential and promising data source for economic modeling in small scale areas of China, which will help to support China's regional economic evaluation.

Gridded GDP Projections Compatible With the Five SSPs (Shared Socioeconomic Pathways)

Historical and future spatially explicit population and gross domestic product (GDP) data are essential for the analysis of future climate risks. Unlike population projections that are generally available, GDP projections—particularly for scenarios compatible with shared socioeconomic pathways (SSPs)—are limited. Our objective is to perform a high-resolution and long-term GDP estimation under SSPs utilizing a wide variety of geographic auxiliary information. We estimated the GDP in a 1/12-degree grid scale. The estimation is done through downscaling of historical GDP data for 1850–2010 and SSP future scenario data for 2010–2100. In the downscaling, we first modeled the spatial and economic interactions among cities and projected different future urban growth patterns according to the SSPs. Subsequently, the projected patterns and other auxiliary geographic data were used to estimate the gridded GDP distributions. Finally, the GDP projections were visualized via three-dimensional mapping to enhance the clarity for multiple stakeholders. Our results suggest that the spatial pattern of urban and peri-urban GDP depends considerably on the SSPs; the GDP of the existing major cities grew rapidly under SSP1, moderately grew under SSP 2 and SSP4, slowly grew under SSP3, and dispersed growth under SSP5.

Firms’ response to macroeconomic estimation errors

Initial Gross Domestic Product (GDP) announcements are important economic signals that convey information on the state of the economy but contain substantial estimation error. We investigate how GDP estimation errors affect firms' real decisions and profitability. We find that GDP estimation errors are positively associated with one-quarter-ahead changes in firms’ capital investments, production, inventory, and profitability. However, we observe long-run profitability reversals, which is consistent with initial over (under) production eventually being met with declines (increases) in future profitability. Furthermore, managerial responses to the estimation errors mimic the response to the true component of the GDP signal, suggesting that managers do not filter estimation errors and overreact to both GDP signal components. Our firm-level findings translate to the macroeconomic level, where we find that a long-run reversal follows a positive short-run aggregate investment response to GDP signal components.

Economic growth on the periphery: estimates of GDPper capitaof the Congress Kingdom of Poland (for years 1870–1912)

AbstractThis paper presents the estimates of the gross domestic product (GDP) of the Congress Kingdom of Poland for the period 1870–1912. The authors used bottom-up methodology and calculated sectoral added values using historical economic, social, and demographic data. The presented results offer first ever insight into the structure of sectoral added values in the Congress Kingdom of Poland during the period of first globalization and first reliable estimates of GDP of the Congress Kingdom of Poland. All results are presented in Geary–Khamis dollars PPP1990 and are compatible with Maddison dataset.

Changes of base-year and Indian GDP growth: an agnostic look

Purpose World 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/approach The 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. Findings The 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 implications The 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/value The 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.

NOWCASTING ‘TRUE’ MONTHLY U.S. GDP DURING THE PANDEMIC

Expenditure-side and income-side gross domestic product (GDP) are measured at the quarterly frequency and contain measurement error. Econometric methods exist for producing reconciled estimates of underlying true GDP from these noisy estimates. Recently, the authors of this paper developed a mixed-frequency reconciliation model which produces monthly estimates of true GDP. In the present paper, we investigate whether this model continues to work well in the face of the extreme observations that occurred during the pandemic year and consider several extensions of it. These include stochastic volatility and error distributions that are fat-tailed or explicitly allow for outliers.

Regional Income Inequality in Lithuania

The problem of income inequality is globally relevant, receiving the attention of both scientists and pol- iticians. Lithuania as a small country has made significant progress in approaching the standard of liv- ing in Western Europe. However, there are still differences in economic growth between separate popula- tion groups. Thus, the problem of income inequality remains very acute. Currently, researchers are widely discussing the risk of income inequality to the country’s society by analysing its causes and proposing var- ious solutions. Although scientific debates address income inequality across regions, such studies are often limited to examples of large countries. Meanwhile, there is a lack of studies on regional income inequality in small countries, so the question of whether a small country is characterised by regional income inequal- ity remains open. This research aims to examine the level of regional income inequality in Lithuania. We hy- pothesised that Lithuania has a high level of regional income inequality and this is one of the causes of the high income inequality in the whole country. To estimate regional income inequality, we used the most com- mon measures: Gini coefficient, decile ratio, and the coefficient of variation. The analysis was performed at level 3 of the Nomenclature of Territorial Units for Statistics (NUTS), according to which Lithuania is divided into 10 administrative counties. For this research, we chose the indicators illustrating income per capita on various levels, i.e., gross domestic product (GDP) per capita, average disposable household income per cap- ita, and gross hourly wages, as the various types of income can be used. For the estimation of GDP per cap- ita and average disposable household income, we analysed the data provided by the Lithuanian Department of Statistics (Statistics Lithuania) for 2014–2017. Due to a lack of data to estimate gross hourly wages, we examined the statistical data from the Lithuanian Department of Statistics for 2014. The results show that the hypothesis has not been confirmed. According to the research results, there is a small distribution of in- come between different regions of a small economy, although the level of economic development of different regions differs. The study findings are important not only from an academic perspective for identifying the causes of income inequality and raising questions for further research, but also for regional economic policy makers. The obtained results show that decisions related to a more equal distribution of income in Lithuania as a small country are determined not only by the specificity of its regions but also by the general trends of the country.

Nowcasting Monthly GDP with Big Data: A Model Averaging Approach

Gross domestic product (GDP) is the most comprehensive and authoritative measure of economic activity. The macroeconomic literature has focused on nowcasting and forecasting this measure at the monthly frequency, using related high frequency indicators. We address the issue of estimating monthly gross domestic product using a large dimensional set of monthly indicators, by pooling the disaggregate estimates arising from simple and feasible bivariate models that consider one indicator at a time in conjunction to GDP. Our base model handles mixed frequency data and ragged-edge data structure with any pattern of missingness. Our methodology enables to distill the common component of the available economic indicators, so that the monthly GDP estimates arise from the projection of the quarterly figures on the space spanned by the common component. The weights used for the combination reflect the ability to nowcast quarterly GDP and are obtained as a function of the regularized estimator of the high-dimensional covariance matrix of the nowcasting errors. A recursive nowcasting and forecasting experiment illustrates that the optimal weights adapt to the information set available in real time and vary according to the phase of the business cycle.

Using aircraft location data to estimate current economic activity

Aviation is a key sector of the economy, contributing at least 3% to gross domestic product (GDP) in the UK and the US. Currently, airline performance statistics are published with a three month delay. However, aircraft now broadcast their location in real-time using the Automated Dependent Surveillance Broadcast system (ADS-B). In this paper, we analyse a global dataset of flights since July 2016. We first show that it is possible to accurately estimate airline flight volumes using ADS-B data, which is available immediately. Next, we demonstrate that real-time knowledge of flight volumes can be a leading indicator for aviation’s direct contribution to GDP in both the UK and the US. Using ADS-B data could therefore help move us towards real-time estimates of GDP, which would equip policymakers with the information to respond to shocks more quickly.

Estimation of GDP from public library usage: Turkey sample

PurposePublic libraries are institutions that provide information services to all citizens in a country and support the development of lifelong learning, awareness and creativity. All these provide an advantage for having a developed economy and being a developed society. This study aims to reveal the interaction between the gross domestic product (GDP) and public library usage in Turkey. The study also purposed to increase awareness of the economic benefits of public libraries.Design/methodology/approachIn the study, a vector autoregression (VAR) model and co-integration techniques were used. Johansen co-integration test was used examining the long-term relationship between the variables. Due to the variables moving together in the long term, the vector error-correction model was preferred instead of the VAR model, as a result.FindingsAccording to the causality test results, GDP was found to be affected when there was a change in the number of library users. According to the Granger causality test result, a change in GDP was also found to have a significant effect on the number of library users. All these indicate an interaction between GDP and public library usage in Turkey between 2001 and 2017.Social implicationsThe revealed interaction between GDP and the number of public library users can be useful for policymakers who are making decisions to develop public library services and to increase GDP.Originality/valueThe interaction between public library usage and GDP can be seen such as an unlikely combination. However, this study presented a mutual interaction between public library usage and GDP. The findings of the study will be of a great importance in developing countries to be motivated to make public library services better.

Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

Проблемы и перспективы использования показателя ВВП в макроэкономическом анализе (Problems and Prospects of Using the GDP Indicator in Macroeconomic Analysis)

Russian Abstract: Предметной областью исследования являются вопросы методологии и методов оценки масштабов валового внутреннего продукта (ВВП). Выявлены основные проблемы методологии построения ВВП на основе альтернативных методических подходов. Представлены алгоритмы согласования разрабатываемых оценок ВВП на сходимость. Рассмотрен и систематизирован перспективный зарубежный опыт корректировок ВВП с учетом экологических и социальных факторов. Представлены рекомендации по его использованию в российской статистической практике. Представлены результаты экспериментальных оценок российского ВВП с учетом корректировок на производственные социальные, экологические компоненты. English Abstract: The subject area of the research is the methodology and methods of estimation the scale of gross domestic product (GDP). The main problems of GDP construction methodology on the basis of alternative methodological approaches are revealed. Algorithms for matching the developed GDP estimates for convergence are presented. The perspective international experience of GDP adjustments taking into account environmental and social factors is considered and systematized. Recommendations for its use in Russian statistical practice are presented. The results of experimental estimates of Russian GDP with adjustments for production social and environmental components are presented.

Estimation of GDP Using Deep Learning With NPP-VIIRS Imagery and Land Cover Data at the County Level in CONUS

Accurate estimation of gross domestic product (GDP) at small geographies is of great significance to evaluate the distribution and dynamics of socio-economic development. Nighttime light (NTL) data is becoming increasingly important in socio-economic data estimation. However, previous research has found that using NTL alone is insufficient to accurately measure the GDP at small geographies, and the contribution of NTL for time-series GDP estimation is unreliable. This article proposed a deep learning method for the Contiguous United States (CONUS) time-series (2012–2015) GDP estimation at county level. The model is developed by combining the NTL data from the visible infrared imaging radiometer suite day/night band and the MODIS land cover data. The proposed method can improve the existing methods mainly in two ways. First, by taking advantage of the great computing power of the Google Earth Engine, a histogram-based feature regulation method was employed, which not only keeps more information over regions but also provides dimension-reduced tensors from mass remote sensing data. Second, a multi-inputs convolutional neural network-based model was proposed instead of the traditional linear regression model for multisource feature exploration and learning. The proposed method was evaluated by leave-one-year-out cross-validation with the time-series (2012–2015) data. The results show that the $R^2$ between the actual and estimated GDP are 0.81, 0.83, 0.83, and 0.83 for years from 2012 to 2015, indicating a good predictive power of the proposed model. Given that the data employed are globally and publicly available, the proposed method would also be applicable in other countries or regions where socio-economic survey data is hard to obtain.