Forecasting for the Russian Economy Using Small-Scale DSGE Models

Kreptsev, Dmitry; Seleznev, Sergei

doi:10.31477/rjmf.201802.51

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…Expecting a price increase, the consumers will probably start demonstrating a higher demand for goods getting rid of their savings and widening the inflation spiral. 4. The retail turnover reflects the commodity weight (in monetary terms) passing into the sphere of private consumption.…”

Section: Methodsmentioning

confidence: 99%

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Artificial Neural Networks in Inflation Forecasting at the Meso-Level

2021

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The aim of the article is to analyze inflation factors and their influence on the consumer price index in the regions. The article discusses the existing mathematical models for the forecasting of the regional inflation rate as an important national measure. Advantages, disadvantages and application fields of these models are presented. The appropriateness of recurrent neural network use for regional inflation forecasting is demonstrated. The article describes the process of neural network architecture creation, its training, inflation parameter forecasting.

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Section: Methodsmentioning

confidence: 99%

“…Inflation factors include interest rates, currency exchange rate, level of consumption and savings, salary, import volume, disbursement level etc. [4] Note that DSGE models operating a large data set are characterized by the high complexity of construction, data collection and determination of relationship between the variables.…”

Section: Methodsmentioning

confidence: 99%

Artificial Neural Networks in Inflation Forecasting at the Meso-Level

2021

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“…The state space is defined by the system of equations in the following form: denotes change in the potential output, i t is the money market interest rate, and ν t and u t stand for normally-distributed estimation errors. The calibrated parameters denoting sensitivities in the system are mostly based on (Kreptsev & Seleznev, 2018) and take the following form:…”

Section: Kalman Filtermentioning

confidence: 99%

Estimates Of Russia’s Potential Output

Maleček

Janíčko²,

Janíčko

2021

ECO

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Taking into consideration the specifics of the Russian economy such as dependency on oil and gas drilling & production, and including the current context of the Western sanctions, COVID-19 pandemic, as well as somewhat idiosyncratic potential output development, the main aim of this paper is to quantify recent output gap for Russia. We use three mainstream methodologies: the Hodrick-Prescott filter as a benchmark, the Kalman filter to follow, and the Cobb-Douglas production function. The sample time span ranges from 1995Q1 until 2020Q3, while all calculations are performed on quarterly frequencies. The analysis suggests that given low fixed investment ratios, limited R&D spending in non-military sectors, and adverse demographic development, under a “no policy change” scenario there might soon be even more downward pressures on the country’s potential output growth, and the economy may continue increasing only at a snail’s pace even after a possible withdrawal of the Western sanctions and the end of the COVID-19 pandemic.

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“…We consider a standard RBC model of a small open economy. See a similar model, but without capital, in the work of Kreptsev and Seleznev (2018).…”

Section: Equilibrium Rate In General Equilibriummentioning

confidence: 99%

“…The model is estimated using the data on real GDP (both in Russia, including expenditure GDP, and the global economy), inflation (in Russia and its trading partners) and the MIACR interest rate. Some parameters are calibrated; see Kreptsev and Seleznev (2018). II IV II IV II IV II IV II IV II IV II IV II IV II IV II IV II IV II IV II 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 The current equilibrium interest rate estimate (according to the second definition) in this model is provided in Figure 5.…”

Section: Equilibrium Rate In General Equilibriummentioning

confidence: 99%

Estimates of the Natural Rate of Interest for Russia: Is Navigating by the Stars Useful?

Porshakov¹,

Sinyakov²

2019

RJMF

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We estimate the natural rate of interest for Russia in the short term and long term using three definitions of the rate and discuss the possible implications of the results for monetary policy. To start with, we consider partial equilibrium (under no-arbitrage condition), which is presented in the papers on estimating the natural rate. The estimates turn out to be extremely sensitive to assumptions about model parameters. The estimates based on the uncovered interest rate parity, though dependent only on observable (market) variables, impose an additional strong assumption of the path of the future equilibrium exchange rate. We supplement these calculations with calculations in panel data (for long-term equilibrium) and using semi-structural methods (for current equilibrium). To get estimates according to the strict definition of the natural rate we estimate a real business cycle model of the resource-based economy with investments using Russian data. All the estimates are highly uncertain. Taking into account the latter, the central bank should use robust monetary policy rules and avoid communicating the natural rate at least until there has been a sufficient history of business cycles in Russia.

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Forecasting for the Russian Economy Using Small-Scale DSGE Models

Cited by 11 publications

References 19 publications

Artificial Neural Networks in Inflation Forecasting at the Meso-Level

Artificial Neural Networks in Inflation Forecasting at the Meso-Level

Estimates Of Russia’s Potential Output

Estimates of the Natural Rate of Interest for Russia: Is Navigating by the Stars Useful?

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