Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model

Porshakov, Alexey; Deryugina, Elena; Ponomarenko, Alexey; Sinyakov, Andrey

doi:10.31737/2221-2264-2016-30-2-3

Cited by 18 publications

(9 citation statements)

References 18 publications

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“…With average annual double-digit economic growth rate over the past four decades, the GDP of South China's Guangdong Province in 2016 reached RMB 8.0 trillion ($US$1.2 trillion), 1,2 putting it on par with the Spanish economy (the global 14th biggest economy) , 3 and approaching Russian GDP (the global 13th biggest economy). (the global 14th biggest economy) 3,4 However, the spectacular economic achievement of Guangdong is also accompanied with huge increase in pollutant, especially carbon pollution. 5 In this paper, we try to better understand the relationship between carbon emission and economic output in Guangdong from the perspective of sectors.…”

Section: Introductionmentioning

confidence: 99%

Decoupling and decomposition analysis of carbon emissions from economic output in Chinese Guangdong Province: A sector perspective

Zhao

2018

Energy & Environment

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South China’s Guangdong Province, the Chinese largest provincial economy and the global 14th biggest economy, has been facing a huge challenge of achieving economic growth without emission growth. Developing new strategy for making economic growth compatible carbon reduction requires better understanding of the decoupling carbon emission from economic growth. In this paper, we conduct a comprehensive decoupling and decomposition analysis of carbon emission from economic output in Guangdong Province from a sector perspective. We firstly calculate carbon emission in six sectors based on the energy consumption of each sector and carbon coefficient of 13 types of fuels during 2000–2014, and then quantify the decoupling status between CO2 emissions and economic growth in those six sectors by using the Tapio decoupling index, finally, investigate the influencing factors of emissions by using the decomposition techniques. The modeling results show that agricultural sector has strong decoupling, industrial, transport and others sectors are weak decoupling; construction and trade sectors are expansive negative decoupling. We also find that energy intensity and economic output are the major factors influencing carbon emission, also the effects of energy structure and emission factor among six sectors are studied. Some policy recommendations finally are put forward.

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Section: Introductionmentioning

confidence: 99%

Decoupling and decomposition analysis of carbon emissions from economic output in Chinese Guangdong Province: A sector perspective

Zhao

2018

Energy & Environment

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“…Alternatively, a dynamic factor model, where factor estimation is performed using the Kalman smoother (see, for example, (Porshakov, 2016)). In the last mentioned work, without revealing the mechanism of country's economy functioning, using the published numerous macroeconomic indicators, a forecast is made of the value of GDP, which will soon be announced by Rosstat (based on the same indicators).…”

Section: Introductionmentioning

confidence: 99%

Consumption in Russia: Real Values and Impact on GDP

Borodachev¹

2018

Preprint

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This paper based on systems - theoretic approach to the definition of a country's GDP as not directly observable characteristic of system state. Leontief dynamic model is generalized to take into account the stimulating effect of consumption on GDP growth. In consumption, apart from final consumption, terms are considered: balance of foreign trade, fictitious investments and hidden costs. The Kalman filter uses Rosstat's gross output (for system output) and final consumption (for system control) data from 1995 to 2015. It is concluded that if in the years 2014, 2015 it was possible to increase consumption by 5% by, say, price cuts or some increase in money supply, then GDP would be greater by about 2.5%. GDP real values in recent years are most likely greater than official values. Fictitious investments and hidden costs are found in the amount of up to third the value of final consumption. The accuracy of one-year forecasts of true GDP by the methodology of this article is approximately 1.5%.

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“…It is possible with Composite coincident indicators (CCI). Arnoštová, et al (2011) and Rusnák (2013) develop cyclical indicators for the Czech Republic, and Porshakov, et al (2015) who build CCI for Russia. Simionescu (2016b) was monitoring economic growth of Romania and V4 and EU member countries (Simionescu et al, 2017b;Simionescu 2016a) and in the collections of authors Simionescu, et al (2017a), they use combined forecasts to improve survey of profession forecasters' predictions for economy of the USA.…”

Section: Introductionmentioning

confidence: 99%

The Composite Leading Indicator for German Business Cycle

Tkáčová¹,

Gavurová²,

Behún³

2017

JOC

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Monitoring and predicting economic cycles have returned to the awareness of economists with the impact of the economic crisis in 2007/2008. To determine the current and future state of the country's economic cycle, Composite Leading Indicators (CLI) can be used. Their structure is being dealt with by institutions at the national and international level (OECD, Eurostat). Correct predictions of public finance development and the entrepreneurship sphere are very important for competitiveness of the country. The aim of the paper is to propose a new Composite Leading Indicator (CLI) to monitor and predict the German economy. The analysis of 140 quantitative and qualitative indicators of industry, services, retail, construction, foreign trade, labor market, money aggregates, stock indices, confidence indicators, consumer expectations was performed for the needs of the indicator. As the reference series represents the German economic cycle, the GDP indicator is selected at constant prices for 2010. All selected quarterly time series are applied with seasonal index methods, the Hodrick-Prescott filter (HP filter) in the R program, cross-correlation with time shifts, methods selection and scoring, data standardization, identification of the same and different data and the subsequent construction of the CLI of the German economic cycle. The generated CLI can predict the German economy cycle two quarters ahead with a cross-correlation value of 0.867. The forecasting capabilities of the assembled indicator were better than the prediction capabilities of OECD, Eurostat and IFO indicator.

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Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model

Cited by 18 publications

References 18 publications

Decoupling and decomposition analysis of carbon emissions from economic output in Chinese Guangdong Province: A sector perspective

Decoupling and decomposition analysis of carbon emissions from economic output in Chinese Guangdong Province: A sector perspective

Consumption in Russia: Real Values and Impact on GDP

The Composite Leading Indicator for German Business Cycle

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