Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita

In this paper, we study economic growth and its volatility from an episodic perspective. We first demonstrate the ability of the genetic algorithm to detect shifts in the volatility and levels of a given time series. Having shown that it works well, we then use it to detect structural breaks that segment the GDP per capita time series into episodes characterized by different means and volatility of growth rates. We further investigate whether a volatile economy is likely to grow more slowly and analyze the determinants of high/low growth with high/low volatility patterns. The main results indicate a negative relationship between volatility and growth. Moreover, the results suggest that international trade simultaneously promotes growth and increases volatility, human capital promotes growth and stability, and financial development reduces volatility and negatively correlates with growth.

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Trade Openness and Economic Growth Nexus: A Case Study of BRICS

Foreign Trade Review ◽

10.1177/0015732518810902 ◽

2018 ◽

Vol 54 (1) ◽

pp. 1-15 ◽

Cited By ~ 5

Author(s):

L. G. Burange ◽

Rucha R. Ranadive ◽

Neha N. Karnik

Keyword(s):

Economic Growth ◽

Time Series ◽

Causal Relationship ◽

Trade Openness ◽

Gdp Per Capita ◽

Jel Codes ◽

Per Capita ◽

The Impact ◽

Econometric Technique

The article analyses a causal relationship between trade openness and economic growth for the member countries of BRICS by using an econometric technique of time series analysis. Member countries of BRICS adopted a series of liberalization reforms, almost simultaneously, from the late 1980s. The article attempts to study the impact of trade openness on their growth in GDP per capita. It captures structural composition of GDP and openness of trade in four aspects, that is, merchandise exports, merchandise imports, services export and services import. In India, the study found growth-led trade in services hypothesis. The article supports the growth-led export and growth-led import hypothesis for China and export- and import-led growth for South Africa. However, no causal relationship was evident for Brazil and Russia. JEL Codes: F43, C22

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Deep Assessment Methodology Using Fractional Calculus on Mathematical Modeling and Prediction of Gross Domestic Product per Capita of Countries

Mathematics ◽

10.3390/math8040633 ◽

2020 ◽

Vol 8 (4) ◽

pp. 633

Author(s):

Ertuğrul Karaçuha ◽

Vasil Tabatadze ◽

Kamil Karaçuha ◽

Nisa Özge Önal ◽

Esra Ergün

Keyword(s):

Time Series ◽

Fractional Calculus ◽

Gross Domestic Product ◽

Gdp Per Capita ◽

Domestic Product ◽

Average Error ◽

Assessment Methodology ◽

Fractional Model ◽

Modeling And Prediction ◽

Per Capita

In this study, a new approach for time series modeling and prediction, “deep assessment methodology,” is proposed and the performance is reported on modeling and prediction for upcoming years of Gross Domestic Product (GDP) per capita. The proposed methodology expresses a function with the finite summation of its previous values and derivatives combining fractional calculus and the Least Square Method to find unknown coefficients. The dataset of GDP per capita used in this study includes nine countries (Brazil, China, India, Italy, Japan, the UK, the USA, Spain and Turkey) and the European Union. The modeling performance of the proposed model is compared with the Polynomial model and the Fractional model and prediction performance is compared to a special type of neural network, Long Short-Term Memory (LSTM), that used for time series. Results show that using Deep Assessment Methodology yields promising modeling and prediction results for GDP per capita. The proposed method is outperforming Polynomial model and Fractional model by 1.538% and by 1.899% average error rates, respectively. We also show that Deep Assessment Method (DAM) is superior to plain LSTM on prediction for upcoming GDP per capita values by 1.21% average error.

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Two concerns about the interpretation of the estimates of historical national accounts before 1850

Journal of Global History ◽

10.1017/s174002282000039x ◽

2021 ◽

Vol 16 (2) ◽

pp. 294-300

Author(s):

Jan Luiten van Zanden ◽

Jutta Bolt

Keyword(s):

Economic Growth ◽

Time Series ◽

Industrial Revolution ◽

Gdp Per Capita ◽

National Accounts ◽

Industrial Growth ◽

Long Term Trends ◽

Per Capita ◽

Low Rate

AbstractAs contribution to the debate about the interpretation of the process of economic growth before the Industrial Revolution, we discuss two concerns about the currently available estimates of historical national accounts and the way in which these estimates should be interpreted. Firstly, we argue that estimates of the long-term trends of economic growth should make use of all information contained in time series of Gross Domestic Product (GDP henceforth), and therefore use standard regression analysis to establish those trends. Secondly, we point to the problem that the time series of historical GDP are based on very different estimation procedures, which probably affect the outcome in terms of the level of GDP per capita in the period before 1850. Both concerns imply that we do not entirely agree with Jack Goldstone’s views of pre-industrial growth. In particular, his conclusion that growth was cyclical before 1800 is inconsistent with the available GDP estimates, which point to sustained growth, albeit at a very low rate.

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Research on Formation of Kondratiev Cycles in Brazilian Economy

Scientific Research and Development Economics ◽

10.12737/12773 ◽

2015 ◽

Vol 3 (4) ◽

pp. 14-20

Author(s):

Шишкин ◽

Andrey Shishkin

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Data Acquisition ◽

Gdp Per Capita ◽

Per Capita ◽

Long Cycles ◽

The 1960S ◽

Research Period

The work is focused around the analysis of formation of Kondratiev cycles from1870 to 2008. This research is directed on data acquisition on the possibility of formation of long cycles on the basis of the spectral analysis of deviations from a trend of GDP per-capita time series. The conducted research resulted in notion that fifty-year cycles of Kondratiev had the greatest power during practically all research period. it was Also established that from the middle of the XX century to the 1960s the power of Kondratiev cycleshad increased. The only segment of research time series on which there is no domination of the cycles is 1870–1969. The research can give the chance to compare the tendency of Kondratievcycles to increase in power and formation of technological waves in the economy of Brazil.

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The Results of the Research of Kondratiev Waves in Economies of Greece and Portugal

Scientific Research and Development Economics ◽

10.12737/17721 ◽

2016 ◽

Vol 4 (1) ◽

pp. 50-56 ◽

Cited By ~ 1

Author(s):

Левкина ◽

Nataliya Levkina

Keyword(s):

Time Series ◽

Regression Analysis ◽

Gdp Per Capita ◽

Economic Dynamics ◽

Real Gdp ◽

Real Gdp Per Capita ◽

Greek Economy ◽

Per Capita ◽

Technological Mode ◽

Of Greece

The article presents the results of the analysis of Greek and Portuguese economic dynamics in order to identify Kondratiev waves, to divide them into periods, and to determine the point of originating ofnew technological modes. in Greek economy,the analysis of the time series of real GDP per capita for 1913–2008 revealed the presence of economic dynamics’cycles with a period of approximately 50 years, which can be identified as Kondratiev cycles (waves).The regression analysis of the time series of Greek and Portuguese real GDP per capita allowed to determine the date of beginning of the fourth and fifth half-waves of Kondratiev cycles and the date of origin of the fifth and sixth technological modes in Greek and Portuguese economies. The obtained results of the analysis showed that the origin of the fifth technological mode in both economies occurred in the 1950s, the sixth — in the late XX century. The results of the research may be used in order to construct models of technological modes’ productivity in Greece and Portugal.

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

Population Health Metrics ◽

10.1186/1478-7954-10-12 ◽

2012 ◽

Vol 10 (1) ◽

Cited By ~ 55

Author(s):

Spencer L James ◽

Paul Gubbins ◽

Christopher JL Murray ◽

Emmanuela Gakidou

Keyword(s):

Time Series ◽

Gdp Per Capita ◽

Per Capita

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Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM2.5 in China During 1998–2016

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16071149 ◽

2019 ◽

Vol 16 (7) ◽

pp. 1149 ◽

Cited By ~ 3

Author(s):

Yi Yang ◽

Jie Li ◽

Guobin Zhu ◽

Qiangqiang Yuan

Keyword(s):

Time Series ◽

Socioeconomic Factors ◽

Urban Population ◽

Gdp Per Capita ◽

Car Ownership ◽

Long Time ◽

Private Car ◽

Per Capita ◽

Spatio Temporal ◽

Pm2.5 Concentration

A comprehensive understanding of the relationships between PM2.5 concentration and socioeconomic factors provides new insight into environmental management decision-making for sustainable development. In order to identify the contributions of socioeconomic development to PM2.5, their spatial interaction and temporal variation of long time series are analyzed in this paper. Unary linear regression method, Spearman’s rank and bivariate Moran’s I methods were used to investigate spatio–temporal variations and relationships of socioeconomic factors and PM2.5 concentration in 31 provinces of China during the period of 1998–2016. Spatial spillover effect of PM2.5 concentration and the impact of socioeconomic factors on PM2.5 concentration were analyzed by spatial lag model. Results demonstrated that PM2.5 concentration in most provinces of China increased rapidly along with the increase of socioeconomic factors, while PM2.5 presented a slow growth trend in Southwest China and a descending trend in Northwest China along with the increase of socioeconomic factors. Long time series analysis revealed the relationships between PM2.5 concentration and four socioeconomic factors. PM2.5 concentration was significantly positive spatial correlated with GDP per capita, industrial added value and private car ownership, while urban population density appeared a negative spatial correlation since 2006. GDP per capita and industrial added values were the most important factors to increase PM2.5, followed by private car ownership and urban population density. The findings of the study revealed spatial spillover effects of PM2.5 between different provinces, and can provide a theoretical basis for sustainable development and environmental protection.

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ECONOMETRIC FORECASTING MODELS FOR AIR TRAFFIC PASSENGER OF INDONESIA

Journal of the Civil Engineering Forum ◽

10.22146/jcef.26594 ◽

2017 ◽

Vol 3 (1) ◽

pp. 303

Author(s):

Viktor Suryan

Keyword(s):

Time Series ◽

Panel Data ◽

Time Series Data ◽

Air Traffic ◽

Gdp Per Capita ◽

Series Data ◽

Economic Variables ◽

Transport Services ◽

Airline Passengers ◽

Per Capita

One of the major benefits of the air transport services operating in bigger countries is the fact that they provide a vital social economic linkage. This study is an attempt to establish the determinants of the passenger air traffic in Indonesia. The main objective of the study is to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of panel data and to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of time series data. This research also predicts the upcoming number of air traffic passenger until 2030. Air transportation and the economic activity in a country are interdependent. This work first uses the data at the country level and then at the selected airport level for review. The methodology used in this study has adopted the study for both normal regression and panel data regression techniques. Once all these steps are performed, the final equation is taken up for the forecast of the passenger inflow data in the Indonesian airports. To forecast the same, the forecasted numbers of the GDP (Gross Domestic Product) and population (independent variables were chosen as a part of the literature review exercise) are used. The result of this study shows the GDP per capita have significant related to a number of passengers which the elasticity 2.23 (time-series data) and 1.889 for panel data. The exchange rate variable is unrelated to a number of passengers as shown in the value of elasticity. In addition, the total of population gives small value for the elasticity. Moreover, the number of passengers is also affected by the dummy variable (deregulation). With three scenarios: low, medium and high for GDP per capita, the percentage of growth for total number of air traffic passenger from the year 2015 to 2030 is 199.3%, 205.7%, and 320.9% respectively.

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The Role of Business Lending in Gdp Growth

Journal of Economics, Finance and Management Studies ◽

10.47191/jefms/v4-i11-27 ◽

2021 ◽

Vol 04 (11) ◽

Author(s):

Nilufar Ilhomovna Sultanova ◽

Keyword(s):

Time Series ◽

Regression Analysis ◽

Private Sector ◽

Gdp Per Capita ◽

Gdp Growth ◽

Per Capita

This study was conducted using regression analysis of changes in the value of GDP per capita as a result of the influence of domestic lending to the private sector (as a percentage of GDP) and the share of entrepreneurship in GDP. The study showed that the change in the volume of domestic loans to the private sector (as a percentage of GDP) has a greater impact on the change in the value of GDP per capita. However, it should also be noted that an increase in factors and an increase in time series can affect the change in this conclusion.

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