Monetary Stabilisation: Modeling and Estimation for the Russian Economy
The paper analyses Russian monetary policy in 2004 – 2012. We present a model that describes short run nonlinear monetary dynamics induced by balance of payments and policy shocks. We consider Central Bank’s international reserves volume as the key factor of monetary and exchange rate stabilization using “ad hoc” monetary rule. Empirical analysis of the model is carried out with Bayesian techniques. Estimation measures the difference in Central Bank’s preferences in crisis and no crisis dynamics.
This paper constructs a DSGE model for an economy with commodity . exports. We estimate the model using Russian data, making a special focus on quantitative effects of commodity price dynamics. There is a widespread belief that economic activity in Russia crucially depends on oil prices, but quantitative estimates are scarce. We estimate an oil price effect on the Russian economy in a general equilibrium framework. Our setup is similar to those of Kollmann (2001) and Dam and Linaa (2005), but we extend their models by explicitly accounting for oil revenues. In addition to standard supply, demand, cost-push, and monetary policy shocks, we include the shock of commodity export revenues. The main objective of the paper is to identify the contribution of structural shocks to business cycle fluctuations in the Russian economy. We found that despite a strong impact on GDP from commodity export shocks, business cycles in Russia are mostly domestically based.
Stabilizing monetary policy in a small open economy is constrained by the open economy trilemma. In this paper we investigate whether foreign exchange market interventions and the Central Bank’s credit rationing at the official rate (CROR) may soften this constraint and improve the results of monetary policy for different monetary regimes. We construct a DSGE model appropriate for analysing the forward-looking behaviour of households facing non-zero probabilities of losing access to financial market and CROR.
We have found significant credit rationing in the quarterly Russian data of 2001:Q1–2014:Q2. The probability of losing access to financial market and the probability of CROR are estimated as 22% and 66%, respectively. Using Russian data of 2001:Q1–2014:Q2 we demonstrate that CROR provoked forward-looking activity in financial market, which led to more Ruble devaluation in the crises of 2008-2009. It improved poor countercyclical performance of two Russian monetary policy rules, whereas made small effect on welfare. Welfare maximization exercises reveal a trade off between low-inflation and high-welfare solutions and favour of a floating exchange rate regime. We found the optimal value of the probability of CROR in both exchange rate-based and Taylor rule-based models but resulting improvement in welfare is very small.