T he article examines Russia's dependence on hightech imported goods. We improve the OECD hightechnology product classification by increasing the level of disaggregation, accounting for new goods, ensuring comparability over time, and differentiating goods by technological level on quite high levels of disaggregation. We describe the major trends in the world market for high-tech goods and identify the leading countries in each sector (most frequently, China, Germany, Republic of Korea, Switzerland, and Singapore) primarily by calculating net exports of high-tech goods in these sectors. We also assess Russian competitive positions in the global market for high-tech goods by sectors, applying the newly developed competitiveness index, and measure Russian dependence on high-tech goods imported from countries that recently imposed sanctions against Russia. We show that Russia's economy is highly dependent on imports of pharmaceutical goods and medical equipment, machinery and equipment (except nuclear reactors, fuel elements, engines and turbines), and electrical equipment. The sectors with most imports originating from 'sanctionimposing' countries are aircraft, medical and optical equipment, engines and turbines, and pharmaceutical goods. Computers and electronic equipment are at the opposite pole: in these sectors, China is the world leader and the key partner for Russia.

We develop an econometric method for estimating risks of financial distress among Russian industrial companies. It deals with international experience in the field of financial stability analysis as well as with the features of Russian data, including the following two. Firstly, bankruptcy is a rare event in the sample of Russian industrial companies. Secondly, not all of the companies become bankrupt by economic reasons. At the same time, not all of the companies that face financial problems become bankrupt. We have tried the developed method using a wide sample of Russian companies (appr. 97 000 firms a year, more than a million firms all in all). As a result, the method shows a significant shift in the ability of the model to predict financial distress risks in comparison to other suitable methods. Results and conclusions made in the paper can help clarify anti-crisis macroeconomic policy as well as reveal Russian industries to focus on.

The article discusses approaches and instruments used in the Bank of Russia public analytical materials for analysis and forecast of macroeconomic conditions and monetary indicators. The authors focus on indicators of business cycle and monetary conditions, as crucial for monetary policy analysis. The attention is paid to issues most frequently discussed in scientific and expert literature, specifically, to new indicators and models presented in the Bank of Russia Monetary Policy Reports in 2015.

The paper discusses dynamics of private sector debt-to-GDP ratio and debt service ratio (DSR). We show that the level of DSR for developing countries is less than that of DSR for developed countries, and has a more volatile dynamics. Developing countries face significant risk from external sector of the economy due to high level of their dependence on external debt - through currency revaluation, on the one hand, and reciprocal growth of interest rates, on the other hand. This is illustrated, for example, by the situation in Russia in 2014-2016. We also show that countries with monetary policy based on inflation targeting face much more downplayed response of DSR shocks on their economic activity than countries with different regimes of monetary policy. That is why currency crises in several regions including South-East Asia and Russia, have led to significant growth in DSR and forwarded shift to inflation targeting in these countries. Along with shocks of DSR related to volatility of foreign currency, we explore those related to inflation and monetary conditions, abrupt changes in economic activity, etc. The paper also focuses on factors of DSR dynamics, including interest rates, terms, volumes, foreign currency revaluation, and its decomposition on the long period of time.

We develop a two-step methodology for analysis of FDI inflows into industries of Russian real sector, based on Heckman selection model. The methodology is related to the following important points: (1) it helps to take into account multilevel factors of FDI inflows (including firm-level, industrial and macroeconomic factors); (2) one can use it to forecast FDI inflows into industries. According to estimates, in 2015 FDI to Russian real sector is going to fall in most industries. This shall result in a significant shift in the FDI structure towards raw materials and energy sectors. However, in 2017 FDI will recover in most industries (including the shift back in the sectoral structure of FDI).

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