Time Series Of Gross Domestic Product Research Articles

Scientific progress in information theory quantifiers

The Bandt and Pompe method (BPM) has been successfully applied to estimate the information theory quantifiers. The most significant limitation of the BPM is that this approach is based on point estimation. This research provides scientific progress into the several applications of the information theory quantifiers. In this way, we propose a new statistic confidence interval estimation method for the Permutation Entropy, Fisher Information Measure, and Macroeconophysics Indicator of Efficiency (MIEE). Therefore, we examine quarterly Gross Domestic Product (GDP) time series for 25 countries members of the Organization for Economic Co-operation and Development (OECD). The periods cover more than 59 years, from Q1-1960 to Q3-2019, with 238 data points. For each GDP time series, we employ the non-parametric method Local Block Bootstrap to provide a stylized fact about an empirical distribution for these data taking into account the information theory quantifiers. Based on the values of the empirical distribution of the information theory quantifiers, we construct the Shannon-Fisher causality plane (SFCP), which allows us to quantify the disorder and evaluate randomness present in the time series of quarterly GDP in each country. In addition, we use information theory quantifiers to rank the most efficient countries in allocating the resources arising from economic growth to generate a virtuous cycle of growth. Also, we apply the Principal Component Analysis (PCA) to check the results of the empirical distribution of the MIEE. We find the PCA promotes the same result as the MIEE. Our results suggest an alternative form of clustering using information theory quantifiers linked to the similarity of countries’ GDPs and the mutual influences of one country’s economic growth on the others.

Human wealth evolution is an accelerating expansion underpinned by a decelerating optimization process

Optimization and expansion are two modes of staged evolution of complex systems where macroscopic observables change at a decreasing, respectively increasing, rate. The number of microscopic variables and their interactions are fixed in the first case but change dynamically in the second. A prime example of evolutionary expansion, Gross Domestic Product (GDP) time series gauge economic activities in changing societal structures, and the accelerating trend of their growth probably reflects a manyfold increase of the human interactions that drive change. Naively, one could think of cultural evolution as the result of an optimization process, and then expect the associated GDP growth to have a decelerating trend. We show how optimization and expansion can coexist by replacing ‘wall clock time’ t as independent variable with a measure of human interactions intensity τ. The latter was introduced in a previous work and is computed using the GDP time series itself.Our analysis of eight centuries of yearly GDP data from three regions of Western Europe, corresponding to present day UK, France and Sweden is carried out in two steps. First, a Monte Carlo algorithm is used to fit the GDP data to a piecewise continuous function comprising a sequence of exponentials with different exponents. These arguably correspond to social and technological stages of societal organization. In a second step, GDP data are plotted vs. τ and shown to display two logarithmic regimes, both decelerating, that are joined by a power-law cross-over period. We connect the end of the first regime and the beginning of the second with the dawn of the Industrial Revolution and the societal impact of new transport, communication and production technologies that became widely available after World War I. We conclude that wealth evolution in terms of τ is a decelerating process with the hallmarks of record dynamics optimization.

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.

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation.

Межстрановые сопоставления безработицы и экономического роста в условиях пандемии COVID-19

Introduction. The factor of a sharp slowdown in economic growth in almost all countries since the beginning of 2020 has been quite atypical. Whereas previously we had seen shocks mainly related to economic processes, this time the “black swan” was a public health emergency – the new coronavirus pandemic COVID-19. A feature of this crisis was the unprecedented measures of states to restrict the movement of citizens, as well as the suspension of the activities of both industrial enterprises and enterprises in the sphere of trade and services. The aim of this work is to make cross-country comparisons of the impact of increased unemployment caused by the COVID-19 pandemic on different countries’ economic growth. Theoretical analysis. The relationship between the actual output gap and potential and cyclical unemployment rates has traditionally been studied according to the well-known law of A. Okun. Okun’s Law can be viewed as a linear algebraic equation for the function of real gross domestic product (GDP). The essence of the law is that with an increase in cyclical unemployment, total output should decrease, since the number of people employed in GDP production falls. Empirical analysis. Cross-country comparisons of economic growth and characteristics of the labor market in Russia, the USA, China, Canada and Germany from 2000 to 2020, including the period of the new coronavirus infection pandemic, were carried out. Results. Based on the analysis of time series of GDP and the unemployment rate, it is shown that, depending on the depth and effectiveness of state support measures for business in terms of maintaining employment, deviations of the actual values of GDP from those calculated in accordance with Okun’s empirical law are observed. The largest and smallest deviations in real GDP changes from the predictions for the first half of 2020 have been recorded in Germany and Canada, respectively.

Exploring the Relationships among Exchange Rate, Foreign Investment and Economic Growth Using Time Series Econometric Approaches: The Case of China’s Guangdong Province

As the Chinese government has been trying to promote the Belt and Road Initiatives and enhance the openness of its economic development, it is important to study and understand the depreciation/appreciation mechanism of Chinese yuan (CNY), especially how this affects or is affected by some key economic growth and openness measures like gross domestic product (GDP) and foreign direct investment (FDI). This paper is just to examine the short-run dynamics and long-run equilibrium relationships among the three important macroeconomic time series of CNY exchange rate, FDI and GDP. Twenty-year annual data from 1996 to 2015 for the top ten cities (in terms of GDP) in Guangdong, one of the economically influential provinces in China, are specifically collected for the study. The panel unit-root test, Johansen-Fisher cointegration test, Granger causality test, and vector autoregression (VAR) model are applied to analyze the data for exploring long-run relationships. Vector error correction (VEC) model and block exogeneity Wald test are also adopted to examine the short-run dynamics. Key research results include that, there is a long-run cointegration relationship among exchange rate, FDI and GDP, FDI and GDP are the long-run Granger causes of exchange rate such that FDI positively affects exchange rate whle GDP negatively influence exchange rate, and FDI is also the short-run Granger cause of exchange rate but the short-run relationship is negative. Discussions, and policy implications and future research directions are presented finally.

Continuous national gross domestic product (GDP) time series for 195 countries: past observations (1850–2005) harmonized with future projections according to the Shared Socio-economic Pathways (2006–2100)

Abstract. Gross domestic product (GDP) represents a widely used metric to compare economic development across time and space. GDP estimates have been routinely assembled only since the beginning of the second half of the 20th century, making comparisons with prior periods cumbersome or even impossible. In recent years various efforts have been put forward to re-estimate national GDP for specific years in the past centuries and even millennia, providing new insights into past economic development on a snapshot basis. In order to make this wealth of data utilizable across research disciplines, we here present a first continuous and consistent data set of GDP time series for 195 countries from 1850 to 2009, based mainly on data from the Maddison Project and other population and GDP sources. The GDP data are consistent with Penn World Tables v8.1 and future GDP projections from the Shared Socio-economic Pathways (SSPs), and are freely available at http://doi.org/10.5880/pik.2018.010 (Geiger and Frieler, 2018). To ease usability, we additionally provide GDP per capita data and further supplementary and data description files in the online archive. We utilize various methods to handle missing data and discuss the advantages and limitations of our methodology. Despite known shortcomings this data set provides valuable input, e.g., for climate impact research, in order to consistently analyze economic impacts from pre-industrial times to the future.

Unfolding the Turbulent Century: A Reconstruction of China's Historical National Accounts, 1840–19121

This paper reconstructs China's economic development between 1840 and 1912 with an estimation of Gross Domestic Product (GDP). It provides for the first time a time series of GDP (per capita) for the late Qing Dynasty (1644–1911), based on sectoral output and value added, in current as well as in constant prices. The present estimation of per capita GDP in the late Qing period comes out higher than previous estimations, but it still suggests low average levels of Chinese living standards. The economy during the late Qing Empire was characterized by a large and growing agricultural sector and displayed only minor structural changes. Only in the beginning of the twentieth century did the economy start to show signs of growth.

Forecasting China’s GDP at the pixel level using nighttime lights time series and population images

China’s rapid economic development greatly affected not only the global economy but also the entire environment of the Earth. Forecasting China’s economic growth has become a popular and essential issue but at present, such forecasts are nearly all conducted at the national scale. In this study, we use nighttime light images and the gridded Landscan population dataset to disaggregate gross domestic product (GDP) reported at the province scale on a per pixel level for 2000–2013. Using the disaggregated GDP time series data and the statistical tool of Holt–Winters smoothing, we predict changes of GDP at each 1 km × 1 km grid area from 2014 to 2020 and then aggregate the pixel-level GDP to forecast economic growth in 23 major urban agglomerations of China. We elaborate and demonstrate that lit population (brightness of nighttime lights × population) is a better indicator than brightness of nighttime lights to estimate and disaggregate GDP. We also show that our forecast GDP has high agreement with the National Bureau of Statistics of China’s demographic data and the International Monetary Fund’s predictions. Finally, we display uncertainties and analyze potential errors of this disaggregation and forecast method.

A century of macro-financial linkages

Purpose The recent international financial crisis and the subsequent Great Recession has underlined the need to gain a better understanding of the linkages between financial factors and the real economy. The purpose of this paper is to explore the contributions of financial shocks to macroeconomic fluctuations in Denmark, USA and Canada over the past century. Design/methodology/approach The paper compiles a new data set with break-adjusted quarterly time series of real gross domestic product (GDP) and six other key macroeconomic indicators for the three countries since 1921. It then explores the time-variation of macro-financial linkages by estimating structural vector-autoregressive models separately for three sample periods: 1921-1949, 1950-1979 and 1980-2014. Findings For all three countries, there seems to have been a non-trivial contribution from financial shocks to volatility in output and unemployment in all sample periods, even in the period 1950-1979, which was characterised by tight regulation of the financial sector and widespread financial stability. These findings underscore the general importance of financial factors to macroeconomic fluctuations. Originality/value The paper is the first to offer regression-based estimates of quarterly real GDP for Denmark for 1921-1947. As a result, quarterly figures for real GDP are now available for Denmark for the entire period 1921-2014. Such long-span time series of quarterly real GDP do not exist for any other European countries.

Finding Chaos in Finnish GDP

The goal of this paper is to analyze the Finnish gross domestic product (GDP) and to find chaos in the Finnish GDP. We chose Finland where data has been available since 1975, because we needed the longest time series possible. At first we estimated the time delay and the embedding dimension, which is needed for the Lyapunov exponent estimation and for the phase space reconstruction. Subsequently, we computed the largest Lyapunov exponent, which is one of the important indicators of chaos. Then we calculated the 0---1 test for chaos. Finally we computed the Hurst exponent by rescaled range analysis and by dispersional analysis. The Hurst exponent is a numerical estimate of the predictability of a time series. In the end, we executed a recurrent analysis and displayed recurrence plots of detrended GDP time series. The results indicated that chaotic behaviors obviously exist in GDP.

Odhady zdrojů a užití hrubého domácího produktu ČR pro roky 1970-1989 v metodice ESA 1995

The estimates of economic development in the countries of the socialist block were carried out in the framework of the so-called Material Product System (MPS). At that time the product was perceived more narrowly than today's gross domestic product (GDP). It included only the material goods and productive services (services that accompanying the circulation of goods). Non-productive services and services provided on non-market basis were not included in the estimates. The authors present their own methodology for data transformation based on MPS into (today's) System of National Accounts (SNA), on the basis of carrying out the reconstruction of the historical time series of the gross domestic product of the Czech Republic in the period between the years 1970 and 1989 in the current methodology. The results of this work will enable other users to perform analysis on significantly longer time series of gross domestic product, including the structure of its uses and resources. The authors primarily base the transformation of the original data on global product and national income (in the MPS methodology) contained in the balance of national income and symmetric input-output tables. Historical time series of GDP for the period has not been estimated for the Czech Republic yet.

Developing a comprehensive time series of GDP per capita for 210 countries from 1950 to 2015

BackgroundIncome has been extensively studied and utilized as a determinant of health. There are several sources of income expressed as gross domestic product (GDP) per capita, but there are no time series that are complete for the years between 1950 and 2015 for the 210 countries for which data exist. It is in the interest of population health research to establish a global time series that is complete from 1950 to 2015.MethodsWe collected GDP per capita estimates expressed in either constant US dollar terms or international dollar terms (corrected for purchasing power parity) from seven sources. We applied several stages of models, including ordinary least-squares regressions and mixed effects models, to complete each of the seven source series from 1950 to 2015. The three US dollar and four international dollar series were each averaged to produce two new GDP per capita series.Results and discussionNine complete series from 1950 to 2015 for 210 countries are available for use. These series can serve various analytical purposes and can illustrate myriad economic trends and features. The derivation of the two new series allows for researchers to avoid any series-specific biases that may exist. The modeling approach used is flexible and will allow for yearly updating as new estimates are produced by the source series.ConclusionGDP per capita is a necessary tool in population health research, and our development and implementation of a new method has allowed for the most comprehensive known time series to date.