This paper studies the monetary policy transmission in the Russian economy. The key question of this research is to determine how monetary policy affects the economy through currency exchange rates. I construct a series of monetary policy surprises for the Russian economy using the high-frequency identification approach. Many papers use futures on interest rates as monetary policy instruments; however, we do not have these futures on the Russian financial market. Therefore, I use different currency futures as monetary surprises because these futures are liquid, and they may reveal market sentiments. I take the dates when the Board of Directors of the Bank of Russia made a decision on the key rate and look at the changes in the currency exchange market in a tiny 30-minute window. Next, I construct a structural vector autoregression model to show the effect of these surprises on macroeconomic variables. In the identification process, I use the external instruments approach à la Gertler and Karadi (2015). Finally, I compare the results with other methods (Cholesky decomposition). I find that a tightening monetary policy significantly increases the bond rate; moreover, the effect on inflation is not immediate, but appears after a couple of months.

In this paper, we develop a framework to study the role of ``extreme'' shocks in Russian data -- shocks that have a magnitude of more than four standard deviations. We find that these shocks are the source of biased estimates of the transmission mechanism which leads to a price puzzle. To show it, we develop a monthly DSGE model which we use as a workhorse in simulation exercises. Our focus is on the role of monetary policy shock. We simulate our model under several assumptions about the shocks (whether they come from the shock decomposition of observable variables or simulations). Then we use simulated data from the DSGE model in proxy SVAR to obtain empirical impulse response. Then we compare these responses to the responses estimated from the DSGE model. If monetary policy shock does not contain any peaked shocks, then SVAR impulse responses coincide with DSGE impulse responses. However, if we add a peaked value of monetary policy shock, we immediately observe a price puzzle.

This paper discusses the impact of monetary policy on financial and macroeconomic variables in Russia. We distinguish two types of monetary policy: (1) that causes by changes in the current rates and (2) that causes by any other reason (such as forward guidance, communication, and central bank information). We find that these two types have distinct effects on financial variables. The first type better explains the variation of interest rates for the entire yield curve. In contrast, the second type explains the variation in the exchange rate and market indices. Moreover, we also show that monetary policy transmission from interest rates to inflation takes about one year but this effect is only temporary.

In this paper, we develop a simple framework to study the optimal macroprudential and monetary policy interactions in response to financial shocks. Our model combines nominal rigidities and capital accumulation, features that have usually been studied separately in previous literature. In our model, we show that agents do not internalise how their asset purchases affect asset prices. Thus, when crises occur, there are fire sales: less demand for capital further reduces prices and agents are worse off. Policy interventions (both monetary and macroprudential) can improve allocations by restricting borrowing ex-ante (during the accumulation of risks and imbalances) and stimulating the economy ex-post (during crises). As a result, we find a complementary relationship between ex-ante monetary policy and preventive macroprudential policy. We also compare this result with a flexible-price model and a frictionless model and conduct several sensitivity analysis exercises.

This paper studies the business cycles of the Russian economy. This paper aims to find which frictions are more important for the Russian economy and, therefore, which sectors should be modelled in more detail. I start with the simple case of a closed economy with four distortions, namely, the efficiency, the labour, the investment and the feasibility wedges. However, a closed economy model fails to explain real business cycles in emerging countries. I extend this model to a small open economy to better fit the Russian economy. I have two main findings. For a closed economy, I find that the efficiency and the labour wedges account for most fluctuations in output and investments. The feasibility wedge can play at best the third role. However, for a small open economy, only the efficiency wedge successfully contributes to business cycles fluctuations. The role of the labour wedge is much smaller.

In this paper, I examine the connection between religion and human capital. I want to find the effect of church attendance on human capital. Moreover, I use a non-standard measure of human capital, instead of years of schooling I use PISA test scores. I solve the problem of reverse causality using the instrumental variables. As the IV I take four groups of control variables: geographical controls, economic controls, religious controls and historical controls. The data about religiosity and PISA tests are taken from different surveys and available on the individual levels. I find that there is a strong correlation between church attendance and PISA scores, which can show the difference in human capital between religious and non-religious people.