Accounting for Post-Crisis Macroeconomic Developments in Russia: A Large Bayesian Vector Autoregression Model Approach

Deryugina, Elena; Ponomarenko, Alexey

doi:10.1080/1540496x.2015.1069125

Cited by 4 publications

(6 citation statements)

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“…Similarly, Banbura et al (2015) build a large-size BVAR with a conjugate prior for the euro area to generate conditional forecasts under the hypothetical paths of real GDP, prices, and interest rates. Deryugina and Ponomarenko (2015) propose a medium-size BVAR for the Russian economy with symmetric Minnesota-type prior to make out-of-sample forecasts of key macroeconomic variables for the presanction period of 2010-2014 that are conditioned on oil prices and euro-area GDP. Here, we do not use conjugate and symmetric priors and do not impose conditions on domestic variables as in Bloor and Matheson (2011) and Banbura et al (2015).…”

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confidence: 99%

“…Here, we do not use conjugate and symmetric priors and do not impose conditions on domestic variables as in Bloor and Matheson (2011) and Banbura et al (2015). Moreover, we improve over Deryugina and Ponomarenko (2015) making more accurate conditional forecasts for the sanctions period of 2014-…”

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confidence: 99%

“…Deryugina and Ponomarenko (2015) argue that conditioning on oil prices and euro-area GDP is insufficient for accurate forecasting of Russian economic activity in 2014. Notably, they acknowledge that their 2014 prediction is on the high side, possibly due to other shocks -and sanctions in particular.…”

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confidence: 99%

See 2 more Smart Citations

Should We Care?: The Economic Effects of Financial Sanctions on the Russian Economy

Pestova

Mamonov

2019

SSRN Journal

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confidence: 99%

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confidence: 99%

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Should We Care?: The Economic Effects of Financial Sanctions on the Russian Economy

Pestova

Mamonov

2019

SSRN Journal

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“…4 The Matlab code is posted on the authors' website. See https://sites.google.com/view/ mikhailmamonov/policy-analysis 5 Similar distributions were used earlier to build a BVAR model of the Russian economy in Deryugina and Ponomarenko (2015), Pestova and Mamonov (2016b), and Demeshev and Malakhovskaya (2016).…”

Section: Financial Shocks and Credit Crises: An Instrumental Approachmentioning

confidence: 99%

Financial Shocks and Credit Cycles

Mamonov¹,

Pankova²,

Akhmetov³

et al. 2020

RJMF

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This paper compares the contribution of internal and external financial shocks to the formation of credit cycle phases using cross- country quarterly data for 27 countries, including advanced and emerging economies, for the period from 1990 through 2019. To conduct comparative analysis, we apply IV Probit models of the credit cycle which take into account the relationship between the credit and business cycles the inertia of the cycles and the non-linearity of the transmission of internal and external financial shocks to the economy through the credit market. In our sample of countries, the transmission of shocks to credit cycle phases proves to be non-linear (a switching effect is observed depending on the time elapsed since the shocks occurred); with the economic effect of the external capital inflow shock being in absolute value twice stronger than that of the bank credit supply shock (on average for the current and subsequent quarters); in turn, the bank credit supply shock is twice stronger than the monetary policy shock. A counterfactual analysis of the role of financial shocks in the formation of the credit cycle in Russia indicates an increase in the effectiveness of the monetary authorities in terms of their ability to control the phases of the credit cycle and, accordingly, a relative decrease in the role of credit supply shocks, while the global financial cycle retains its dominance.

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“…This is related to difficulties with re-estimation and with the introduction of observed initial conditions. Therefore, we also utilize a hybrid two-step procedure that Deryugina and Ponomarenko, 2015). Namely, we combine the Minnesota prior, the "sum-of-coefficients" prior and the "dummy-initial-observation" prior, using the vector of hyperparameters from Giannone et al (2015).…”

Section: Abm-based Forecastsmentioning

confidence: 99%

Forecasting the Implications of Foreign Exchange Reserve Accumulation With an Agent-Based Model

2018

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We develop a stock-flow-consistent agent-based model that comprises a realistic mechanism of money creation and parametrize it to fit actually observed data. The model is used to make out-of-sample projections of broad money and credit developments under the commencement/termination of foreign reserve accumulation by the Bank of Russia. We use direct forecasts from the agent-based model as well as the two-step approach, which implies the use of artificial data to pre-train the Bayesian vector autoregression model. We conclude that the suggested approach is competitive in forecasting and yields promising results.

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Accounting for Post-Crisis Macroeconomic Developments in Russia: A Large Bayesian Vector Autoregression Model Approach

Cited by 4 publications

References 10 publications

Should We Care?: The Economic Effects of Financial Sanctions on the Russian Economy

Should We Care?: The Economic Effects of Financial Sanctions on the Russian Economy

Financial Shocks and Credit Cycles

Forecasting the Implications of Foreign Exchange Reserve Accumulation With an Agent-Based Model

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