Estimates Of GDP Research Articles

A concise review towards a novel target specific multi-source unsupervised transfer learning technique for GDP estimation using CO2 emission data

Though economic growths of most of the nations have seen exponential rise due to industrialization, it has also caused proportional increase in their carbon emissions. This paper exploits this proportionate relationship of carbon emission with GDP to predict the per-capita GDP of those nations whose GDP values are missing in the world bank database. The reason behind the same was, those countries were either war-torn or politically isolated/unstable. To achieve the objective of predicting the missing GDP values of those countries from their carbon emissions, this paper exploits the non-linear relationship among the carbon emissions from solid fuels, liquid fuels, and gaseous fuels. It is so because even the differential utilization of these fuels impact economy differently. Use of traditional solid fuel for cooking points toward energy poverty, and access to clean cooking gas indicates higher living standard. However, the available data from the war-torn or isolated countries are very little, and hence insufficient for building a robust predictive machine learning model. So, this paper employs multi-source unsupervised transfer learning to precisely estimate the missing per-capita GDP of those nations. It suitably enlarges the training domains for the prediction models to be more robust. We empirically evaluate the proposed methodology for different regression techniques to estimate the missing GDP values of eleven different nations belonging to diverse strata of economies viz. developed economies, developing, and/or least developing economies. Proposed methodology profoundly improves the prediction preciseness of these regression techniques in estimating the missing per-capita GDP of the considered nations.

Catching-Up and Falling Behind: Russian Economic Growth, 1690s–1880s

We provide decadal estimates of GDP per capita for the Russian Empire from the 1690s to the 1880s, making it possible for the first time to compare the economic performance of one of the world’s largest economies with other countries. Significant Russian economic growth before the 1760s resulted in catching-up on northwest Europe, but this was followed by a period of negative growth between the 1760s and 1800s and stagnation from the 1800s to the 1880s, leaving late-nineteenth century Russia further behind the West than at the beginning of the eighteenth century.

Ecological study measuring the association between conflict, environmental factors, and annual global cutaneous and mucocutaneous leishmaniasis incidence (2005-2022).

Cutaneous and mucocutaneous leishmaniasis (CL/ML) cause significant morbidity globally and are vulnerable to changes from environmental events and conflict. In this ecological study, we aim to measure the associations between annual CL/ML cases, conflict intensity, and environmental factors between 2005 and 2022 globally. We pulled annual case data from the WHO for 52 nations that had conflict intensity scores (ranging from 1-10) from the Bertelsmann Transformation Index. Using Earth observation tools, we gathered temperature, precipitation, vegetation, and humidity data, in addition to data on annual estimates of population, internal displacement, and GDP. We fit a negative binomial generalized additive model with a random nation-level intercept. Conflict was positively associated with increased CL/ML across the studied nations (IRR: 1.09, 95% CI: 1.01-1.16, p = 0.02). Given this, intense conflict (a score of ten) was associated with over double the risk of CL/ML compared to the lowest conflict levels (score of one). We also identified a curvilinear relationship between mean temperature and cases, as well as between vegetation level and cases. Each had small pockets of significant increased and decreased risk, respectively. Larger mean humidity ranges were negatively associated with cases. Importantly, the relationship between conflict intensity and cases was mediated by displacement. Conflict is significantly associated with increased CL/ML cases. This is especially true at higher conflict levels, marking when conflict turns violent. The destruction of critical infrastructure (e.g., that related to healthcare, water, and sanitation) often seen during conflict could drive this association. Such environments can be hospitable to sandflies and can heighten individuals' vulnerability through increased malnutrition, poverty, and displacement. Understanding this relationship is crucial for public health preparedness and response, especially as conflicts become increasingly violent and protracted.

N.D. Kondratiev Long Wave Models for Three Large European Countries

The purpose of this study is to analyze the similarities and differences in the structure of N. D. Kondratiev long waves in the economies of the three largest European countries: Germany, France and Great Britain. It is assumed that the main trend in the dynamics of the GDP of these countries – ​is the trend associated precisely with changes in technology, and not with the influence of ones or others exogenous factors – ​can be represented in the form of a superposition of long Kondratiev waves represented by a periodic function, the values of which at a given time determines the amount of value added produced over the previous time interval. Each wave is characterized by four parameters: time, length of its period, magnitude of the initial amplitude and rate of amplitude decrease. In addition, such characteristics of long waves as the duration of their latency period, the time of their entry into leading positions and duration of their leadership relative to the previous and subsequent technological waves are calculated. Five long Kondratiev waves are considered, the parameters of which are estimated in such a way as to obtain the best description of the dynamics of the GDP of a given country in terms of the deviation of the model from this dynamics. As an estimate of GDP, its values measured in international dollars (Geary–Khamis dollars) for the period from 1790 to 2020 are considered. The calculations showed that in some parameters there is a similarity in the structure of technological waves in these countries, while in others there are differences in the dynamics of their occurrence and development. In the final part of the article, the characteristics of long waves for three European countries are compared with the characteristics of long waves in the US economy over the same time period obtained in previous studies. It is also shown that the leadership durations of long waves obtained in these calculations coincide with the theoretical estimates of the periods of long waves by N. D. Kondratiev.

Reinventing perished “Belgium of the East”: new estimates of GDP for inter-war Latvia (1920–1939)

Reinventing perished “Belgium of the East”: new estimates of GDP for inter-war Latvia (1920–1939)

High-dimensional conditionally Gaussian state space models with missing data

We develop an efficient sampling approach for handling complex missing data patterns and a large number of missing observations in conditionally Gaussian state space models. Two important examples are dynamic factor models with unbalanced datasets and large Bayesian VARs with variables in multiple frequencies. A key observation underlying the proposed approach is that the joint distribution of the missing data conditional on the observed data is Gaussian. Furthermore, the inverse covariance or precision matrix of this conditional distribution is sparse, and this special structure can be exploited to substantially speed up computations. We illustrate the methodology using two empirical applications. The first application combines quarterly, monthly and weekly data using a large Bayesian VAR to produce weekly GDP estimates. In the second application, we extract latent factors from unbalanced datasets involving over a hundred monthly variables via a dynamic factor model with stochastic volatility.

Estimating GDP growth using VIIRS night-time light data

ABSTRACT The accuracy of GDP per capita growth rate in countries with low statistical capacity has been questioned, which hinders understanding progress of Sustainable Development Goals (SDGs) accurately. This study aims to estimate GDP per capita growth rate in countries with poor statistical quality. To achieve this goal, the study builds an econometric model with processed Visible Infrared Imaging Radiometer (VIIRS) night-time light data and other economic variables and the study further assesses the accuracy of the model by comparing the estimated GDP growth to high-quality economic data from Organization for Economic Co-operation and Development (OECD) countries between 2014 and 2018. First, due to the abnormal fluctuations in the time series generated from yearly composite night-time light images, we propose a trend analysis method for the monthly night-time light data to synthesize a more stable yearly time series. Then, we construct artificially low-quality GDP simulations and combine VIIRS data and official statistics data to construct regression models to correct the simulated GDP. Finally, to assess the model accuracy, we compare its estimates of GDP per capita growth rates to official data from high-quality OECD countries, looking for any deviations. We have two major findings as follows: first, the VIIRS data, which comprise information on human activity, are a valuable resource for estimating GDP growth; second, the GDP estimation model can reduce the artificial bias of the simulated GDP per capita growth rate by using VIIRS night-time light data and the average accuracy of the model with simulated data can reach 89%.

Country risk and its impact on renewable energy investment in highly polluted economies.

The country's risk may significantly impact every sector of the economy, and the energy sector is no exception. However, no past study has empirically tested the relationship between country risk and renewable energy investment. Therefore, this study is an effort to investigate the relationship between country risk and renewable energy investment in highly polluted economies. We have employed different econometric techniques to analyze the relationship between renewable energy investment and country risk, including the OLS, 2SLS, GMM, and panel quantile regressions. The estimate of country risk influence renewable energy investment negatively in OLS, 2SLS, and GMM models. Similarly, the country's risk negatively impacts the renewable energy investment from the 10th to 60th quantiles in the panel quantile regression model. Moreover, the GDP, CO2 emissions, and technological development help promote renewable energy investment in OLS, 2SLS, and GMM models, while the human capital and financial development do not significantly impact the renewable energy investment. Furthermore, in the panel quantile regression model, the GDP and CO2 emission estimates are positively significant almost at all quantiles, and the estimate of technological development and human capital are positively significant at higher quantiles only. Therefore, the authorities in highly polluted economies should consider the respective countries' risk considerations while formulating rules about renewable energy.

The D-model for GDP nowcasting

The paper provides a disaggregated mixed-frequency framework for the estimation of GDP. The GDP is disaggregated into components that can be forecasted based on information available at higher sampling frequency, i.e., monthly, weekly, or daily. The model framework is applied for Greek GDP nowcasting. The results provide evidence that the more accurate nowcasting estimations require (i) the disaggregation of GDP, (ii) the use of a multilayer mixed-frequency framework, and (iii) the inclusion of financial information on a daily frequency. The simulation study provides evidence in favor of the disaggregation into components despite the inclusion of multiple sources of forecast errors.

Estimation of the potential GDP by a new robust filter method

The first purpose of this paper is to propose a theoretically new robust filter method to estimate non-observable macroeconomic indicators. The second purpose is to apply the proposed method to estimate the Hungarian potential GDP in 2000–2021. The novelty of the proposed filter method is that — unlike papers published so far — it does not require the stability of the dynamic model, only a partial stability condition must be satisfied. Moreover, such time-dependent uncertainties and nonlinearities can arise in the model that satisfy a general quadratic constraint. An important advantage of the proposed robust filter method over the traditional Kalman filter is that no stochastic assumptions is needed that may not be valid for the problem at hand. The proposed filter method has never been applied to estimate the potential GDP. To estimate the Hungarian potential GDP, the proposed method is applied using uni-, bi- and trivariate models. Estimations up to 2021 has not been published yet for the Hungarian economy. The examined period includes both the financial world crisis and the Covid-19 crisis. The results of the different models are consistent. It turned out that the economic policy was very procyclical after 2012, and the GDP gap was still positive during and also after the Covid-19 crisis.

Communicating Data Uncertainty: Multiwave Experimental Evidence for UK GDP

AbstractEconomic statistics are commonly published without estimates of their uncertainty. We conduct two waves of a randomized controlled online experiment to assess if and how the UK public understands data uncertainty. A control group observes only the point estimate of GDP. Treatment groups are presented with alternative qualitative and quantitative communications of GDP data uncertainty. We find that most of the public understands that GDP numbers are uncertain. Quantitative communications of data uncertainty help align the public's subjective probabilistic expectations of data uncertainty with objective estimates, but do not decrease trust in the statistical office.

The main patterns of economic development in conditions of global instability

With global instability and constant changes on the world stage, forming national economic activity becomes a critical task for every country. Favorable economic environment and fair opportunities for all citizens are the basis of stable development and inclusive growth. Embarking on this path, the country faces numerous challenges, such as the changing geopolitical situation, economic turbulence, social inequalities, and other factors that affect its economy. The purpose of the article is to research the main patterns of economic development in conditions of global instability. Modeling patterns of changes in GDP indicators using trend equations is based on statistical methods for analysis and forecasting of economic processes. The results of the calculations showed that in forecast estimates of nominal GDP, for forecast estimates of real GDP in conditions of global instability and war in Ukraine, the value of forecast estimate I can be considered unrealistic, forecast estimate II corresponds to the period of post-war recovery, and forecast estimate III characterizes the possible estimated value of real GDP of Ukraine after the completion of reconstruction processes and corresponds to the beginning of the next cycle of economic growth. In other words, it is possible to claim that as soon as the real GDP of Ukraine reaches the level of the forecast estimate III (5298029 million UAH), it is possible to consider that we have returned to the previously formed patterns of changes in GDP and economic growth. It is proven that price indices become even more important in the conditions of global challenges, such as economic instability and world crises. This is because they can help forecast market trends and make important decisions regarding the economy's development and the state's fiscal policy. In particular, the consumer price index can influence the state's monetary policy, and the producer price index can indicate the state of production and exports in the country. Important indicators reflecting the level of inflation in the country and the state of the economy are indices of consumer prices and manufacturers of industrial products. They play an essential role in the system of inclusive development because inflation can affect the population's standard of living and income distribution. Moreover, the index of consumer prices and prices of producers of industrial products can indicate the general economic situation in the country and the world, as well as the level of inflation. Keywords: development of the national economy, regularities, global instability.

Green GDP and Economic Openness in India

Aim: The vast range of weather conditions in India's climate system is a result of numerous reasons, including growing agricultural activity, altered land use patterns that increased emissions of methane and nitrous oxide as well as other greenhouse gases, automobile use, and so forth. It is necessary to quantify the significant and under-estimated impact of climate change on the natural system through changes in extreme weather. To account for the economic losses of the environment as a result of climate change, the Green GDP concept was introduced. The objective of the paper is to compute the Green GDP for India and test the effect of economic openness on GDP and Green GDP.
 Methodology: The World Bank Database, the OECD, and Energy Statistics India 2021 are some of the sources from which information was gathered. Growth Rate, Economic Openness Index and the Green GDP estimation methods are employed. To compute the Green GDP indicator for the years 2011 to 20, the GDP measure is subtracted from the costs associated with the utilization of natural resources and environmental degradation.
 Results: According to the outcome, the cost of environmental damage will decline from 11 per cent in 2011 to 9 per cent in 2020. The cost of environmental damage growth rate was -3.07 percent. The Economic Openness Index was 55.62 in the year 2011 and it is gradually declining and attained 36.46 in the year 2020. The lower the index, the lower the impact of trade on domestic activities and the less powerful the economy of that country. The relationship between economic openness and Green GDP per capita shows that less and less economic openness moves the cost of environmental damage to the increasing side.
 Conclusion: The cost of environmental damage increased in the year 2020 compared to 2011 but the growth rate shows negative. The government should take several initiatives to assure data availability and accountability to estimate the environmental damage cost and to frame suitable policy options to improve economic performance overall.

A New Approach to Estimating the Natural Rate of Interest

AbstractBuilding upon the insight that M1 velocity is the permanent component of nominal interest rates—see Benati (2020)—I propose a novel, and straightforward approach to estimating the natural rate of interest, which is conceptually related to Cochrane's (1994a) proposal to estimate the permanent component of Gross National Product (GNP) by exploiting the informational content of consumption. Under monetary regimes (such as inflation‐targeting) making inflation I(0), the easiest way to implement the proposed approach is to (i) project the monetary policy rate onto M1 velocity—thus obtaining an estimate of thenominalnatural rate—and then () subtract from this inflation's sample average (or target), thus obtaining therealnatural rate. More complex implementations based on structural vector autoregressions (VARs) produce very similar estimates. Compared to existing approaches, the one proposed herein presents two key advantages: (i) under regimes making inflation I(0), M1 velocity is equal, up to a linear transformation, to the real natural rate, so that the natural rate is, in fact,observed; and (ii) based on a high‐frequency estimate of nominal GDP, the natural rate can be computed at the monthly or even weekly frequency. In the U.S., Euro area, and Canada, the natural rate dropped sharply in the months following the collapse of Lehman Brothers. Likewise, the 1929 stock market crash was followed in the U.S. by a dramatic decrease in the natural rate.

Quarterly GDP Estimates for the German States: New Data for Business Cycle Analyses and Long-Run Dynamics

Quarterly GDP Estimates for the German States: New Data for Business Cycle Analyses and Long-Run Dynamics

Updated estimates of UK GDP from the income side, 1841–1920

AbstractThis article brings together some of the improvements to GDP estimates from the income side since the publication of Charles Feinstein's 1972 volume National income, expenditure and output of the United Kingdom, 1855–1965. Many of the improvements and refinements were made by Feinstein himself and this paper makes a start in bringing the different elements together, focusing chiefly on reconstructing the income‐based estimates for the period 1841–1920. The new data are then used to comment on several features of the late nineteenth‐century UK economy, considering both the trend and cyclical path of the economy. The new data, coupled with modern de‐trending methods, suggest that there was a long‐term slowing of the UK economy from the late nineteenth century, starting from the 1870s. To undertake the trend–cycle decomposition, we employ a wavelets methodology to describe the time–varying features of trends and cycles over this period.

Методология расчета ВВП: теоретический и практический подходы

The article focuses on the distinction between theoretical and practical approaches to GDP calculation. One of its main provisions is that all costs in the economic society are ultimately aimed at the production of final goods. The theoretical approach takes into account only direct labour, while the practical approach takes into account the costs of both living and past labour embodied in capital goods. The difference is explained, on the one hand, by the special nature of participation of capital goods in the creation of the value of a new product, on the other hand, by the need to have reliable estimates of the produced product. It is demonstrated that the special participation of past labour in the creation of a new product leads to a temporary shift in the cost of new products by the year of production of the capital goods, which does not allow obtaining correct estimates of the produced product in any of the years of use of the capital good. At the same time, taking into account the costs of both living and embodied labour makes it possible to have accurate estimates of the product produced throughout the entire period of use of the capital good, with the exception of the production year of the capital good. The practical approach is linked to the presence in the GDP composition the value of goods which are not directly related to the welfare – gross accumulation, which requires adjustments of the obtained GDP estimates before using them to estimate the well-being of the members of the economic society.

Correlation-Regression Analysis of Innovation Factor Influence on GDP Growth

Introduction. In order to improve innovation policy given a clear definition of the correlation between innovation-driven development indicators and GDP, it is necessary to critically assess the influence of the innovation factor on the country’s economic growth and an increase in its competitiveness.Problem Statement. Modern conditions of the global economic system development require the constant adjustment and improvement of innovation strategies given new civilizational challenges, as well as the intensification of R&D activities of national economies. This highlighted the problem of scholarly research on the influence of innovation andthe determination of correlation between indicators of innovation-driven development and GDP.Purpose. The research is based on the proposed explanatory model built with the use of correlation-regression analysis, which demonstrates the influence of the innovation factor on GDP growth. The article contains forecast estimates of GDP in terms of years, depending on changes in innovation-driven development indicators based on multiple regression analysis, as well as practical recommendations for increasing the innovation capacity of Ukraine.Material and Methods. To identify the impact of innovation on GDP growth, a mathematical model has beenbuilt with the use of correlation-regression analysis; to demonstrate and visualize the results of the study we haveused tabular and graphical methods.Results. To improve the strategy of innovation-driven development of Ukraine, an economic-mathematical model based on multiple regression analysis has been developed. It demonstrates the influence of the innovation factoron changes in GDP. This model helps to understand the functioning and development of innovation processes, toreveal the causal and functional relationships of individual indicators of innovation-driven economic development,as well as to determine the required level of innovation factor by maximizing the hidden innovation capacity.Conclusions. The basic of the strategic course, its basic principle should be the implementation of governmentpolicy aiming at choosing an innovation strategy of structural transformation of Ukraine as a competitive hightech state. This strategy requires the use of domestic R&D potential and makes its contribution to the innovationdriven development of the national economy a crucial factor.

Serbia on the path to modern economic growth

AbstractGiven the scanty and inadequate studies on Serbia's growth performance before the First World War, this paper presents production‐side GDP estimates for Serbia for six years between 1867 and 1910. It probes into the growth dynamics, assessing convergence with the more developed countries of north‐western Europe, as well as progress towards achieving modern economic growth. Although the economy showed some dynamism in terms of overall GDP, per capita GDP in pre‐First World War Serbia grew by only 0.28 per cent per annum, as much of the overall GDP growth was eroded by rapid population growth. Far from converging with north‐western Europe, Serbia continued to fall behind. Sluggish structural transformation and slow income per capita growth suggest that Serbia's transition to modern economic growth was in its infancy. Growth in the dominant agricultural sector was extensive, driven by expanding arable land and population growth. Land was affordable and easy to obtain; hence, peasants invested little in new technologies. Meanwhile, the modern industrial and service sectors were below a threshold that could sustain rapid growth. Nevertheless, this study also highlights the rapid expansion of a small modern sector and export diversification that reflected emergent ‘green shoots’ in 1905–10.

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the proposed indicators, we first design a nowcasting experiment in which we recursively generate estimates of GDP at the end of each quarter, using the latest business and consumer survey data available. Second, we design a forecasting exercise in which we iteratively re-compute the sentiment indicators in each out-of-sample period. When evaluating the accuracy of the predictions obtained for different forecast horizons, we find that the evolved sentiment indicators outperform the time-series models used as a benchmark. These results show the potential of the proposed approach for prediction purposes.