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

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

doi:10.2139/ssrn.2616248

Cited by 17 publications

(12 citation statements)

References 14 publications

(12 reference statements)

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“…Work on forecasting Russian GDP growth is surprisingly scarce (Rautava, 2013;Porshakov, 2016;and Dahlhaus et al;2017). This paper makes several contributions to this area of study.…”

Section: Discussionmentioning

confidence: 99%

“…Most variables are at monthly frequency. Indicators that come at daily or weekly frequency are included in monthly averages following Porshakov et al (2016). We employ most variables in levels (if stationary) and in year-on-year growth rates.…”

Section: Datamentioning

confidence: 99%

“…Rautava (2013) employs a structural vector error-correction macro model built for forecasting purposes and finds that the sharp contraction in Russian output in 2009 can be explained by oil prices and excess uncertainty. Porshakov et al (2016) use a dynamic factor model and a set of 116 indicators to short-term forecast and nowcast quarterly Russian GDP for the period 2012-2014. They find that the model generally outperforms simple benchmark models in terms of predictive accuracy and that new statistical releases of monthly indicators tend to consistently improve predictive accuracy, at least for the nowcast horizon.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data

Mikosch¹,

Solanko²

2019

RJMF

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This paper presents a pseudo real-time out-of-sample forecast exercise for short-term forecasting and nowcasting quarterly Russian GDP growth with mixed-frequency data. We employ a large set of indicators and study their predictive power for different subperiods within the forecast evaluation period 2008-2016. Four indicators consistently figure in the list of top-performing indicators: the Rosstat key sector economic output index, the OECD composite leading indicator for Russia, household banking deposits, and money supply M2. Aside from these indicators, the top indicators in the 2008-2011 evaluation period are traditional real-sector variables, while those in the 2012-2016 evaluation period largely comprise monetary, banking sector and financial market variables. We also compare the forecast accuracy of three different mixed-frequency forecasting model classes (bridge equations, MIDAS models, and U-MIDAS models). Differences between the performance of model classes are generally small, but for the 2008-2011 period MIDAS models and U-MIDAS models outperform bridge equation models.

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“…Work on forecasting Russian GDP growth is surprisingly scarce (Rautava, 2013;Porshakov, 2016;and Dahlhaus et al;2017). This paper makes several contributions to this area of study.…”

Section: Discussionmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data

Mikosch¹,

Solanko²

2019

RJMF

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“…They find that although the BVAR model has relatively better predictive ability for real sector variables, it is outperformed by a random walk specification in most cases. Porshakov et al (2015) seek to predict Russian Federation GDP using a DFM with many macroeconomic and financial variables. The paper focuses on short-term forecasts, nowcasts, and backcasts on a quarterly basis.…”

Section: Literature Reviewmentioning

confidence: 99%

Quarterly Forecasting Model for India’s Economic Growth: Bayesian Vector Autoregression Approach

Gupta

Iyer

2019

SSRN Journal

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The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area, or by using the term "country" in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area.

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“…In recent years, dynamic factor models were used more intensively, in order to build short-term a CCI, used for the purpose of now casting. These applications include Arnoštová et al and Rusnák who develop cyclical indicators for the Czech Republic, and Porshakov et al who build CCI for Russia [15,52,53]. Dynamic factor model is also being more widely used for examining business cycle properties in developed countries (see for example [54][55][56][57][58]).…”

Section: Composite Indicatormentioning

confidence: 99%

The Composite Coincident Indicator (CCI) for Business Cycles

2018

APH

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The aim of this paper is to introduce a composite coincident indicator for the Slovak economic cycle. The theoretical part of the work is concentrated on the analysis of current monitoring options of economic cycle using GDP, industrial production index and composite indicators. The analytical part of the work pays attention to authors' own composite coincident indicator and to testing its monitoring capabilities. Methodology used in this paper is a combination of the methodologies by the OECD, Conference Board and the authors of this paper. Such methods as seasonally adjusted time series, Hodrick-Prescott filter, standardization and cross-correlations are used as applied to 134 indicators of Slovak economy. Coincident cyclic indicators are selected from them, as they form the basis for Slovak composite coincident indicator. The resulting coincident composite indicator for Slovakia is made up of four indicatorsemployment in manufacturing, export of goods and services, the number of hours worked in industry and industrial turnover. The assembled indicator is then tested over time and a need for change in its composition as a result of significant economic changes is grounded. Significant changes in the composition of coincident cyclical indicator for 2010 are also proved. For the period of 2010-2015 a new composite coincident indicator is suggested of the composition of production index in the construction industry, a factor limiting production -insufficient demand, unemployment in the age group of 25-74 y.o. and short-term interest rate.

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

Cited by 17 publications

References 14 publications

Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data

Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data

Quarterly Forecasting Model for India’s Economic Growth: Bayesian Vector Autoregression Approach

The Composite Coincident Indicator (CCI) for Business Cycles

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