Methodology: DEA estimators of total factor productivity and its components; Kalman filter to estimate potential output and output; SFA estimators of cost efficiency of Russian banks; Bayesian vector autoregression with identification for structural shocks; econometric models based on modified Hausmann-Klinger approach to estimate prospective sectors of import substitution and export. The analysis includes the use of statistical and illustrative techniques, as well as other analytical methods (cluster analysis, ranking etc).
Empirical base of research: The data we used include databases by Rosstat, Minfin, Bank of Russia, Ministry of Industry and Trade, Ministry of Economic Development, Federal Customs Service, industrial surveys compiled by IET, newspaper articles, official sources. Laboratory staff created a unique statistical database on Russian economy comprising over five thousand series and makes use of it for research purposes. Also we use the following databases: Cbonds-PRO RU (database on Russian corporate paper issuance), Loans-PRO (database on syndicated credit), BIR-Analytik (database on accounting reports of Russian companies) and several smaller databases.
Results of research:
1. The following results were achieved in the development of methodology
1.1 We improved the methods of quantitative analysis and production capacity estimation. A modelling classification was offered for non-structural macroeconomic forecasting models, with three generations of those. First generation is basic models for univariate (ARIMA) and multivariate (DFM and VAR) time series. Second generation is BVAR. Third generaion is hybrid models combining first generation form and second generation improvements (DFM and VAR with time-varying coefficient models). For Russian economy a specification of a BVAR was offered. First results is analysis of shocks of different nature in MATLAB: confidence shocks for world financial markets, monetary policy (emission) shock. IRFs for key macro indicators were studied.
1.2 We developed a methodology to apply non-parametric production function estimates (DEA) and SFA estimates to the global production capacity frontier. New databases for OECD+Russia and the world’s largest economies were developed. Also a new empirical estimation strategy was offered taking into account the factors that influence production efficiency.
1.3 We estimate potential output growth and output gap for US, euro area, China, and Japan with a variety of multivariate Kalman filters. The main difference from RS-11 is that we use financial variables. Also, we use quarterly data frequency instead of yearly. We detect structural breaks on post-crisis period for all countries with Bai-Perron and Chen-Liu tests.
1.4 We developed a methodology for borders of stability for regional debt with iterative reaction function model with differing thresholds, and we provided criteria for the choice. The results are that debt stability thresholds are around 55% or 84%.
1.5 In this chapter we propose a modification of an industry-wide banking competition indicator, namely the Boone indicator, which does not require information about the interest rates on loans, as opposed to the more popular Lerner index. In previous research, the Boone indicator was estimated as homogeneous effect of the banks marginal costs on their market shares or profitability. We show that this effect is heterogeneous and that the main sources of bank-level heterogeneity of that effect for Russian banks include differences in business models pursued (retail vs. corporate) and credit risk exposures. Based on these differences we identified more risky and less risky niches on credit market. Bank-level estimates of the Boone indicator showed that government-owned banks hold leading positions in every such niche. Next, based on these estimates we, first, confirm monopolistic nature of credit market with Sberbank being the dominant player. Second, we show that banks compete in this market mainly on quality rather than on quantity. Finally, we show that Boone indicator and the Lerner index provide similar predictions about the market power of Russian banks, despite of their differences.
1.6 We improved methods to estimate prospective sectors for import substitution and export growth for Russia (based on modified Hausmann-Klinger analysis). We used data on external trade for 2013 on 4 digits of HS (UN Comtrade). The methods are the same both for import substitution and export growth to compare the results. We offer indicators to align model estimates with data trends, namely shares in world export and import and trade imbalances coefficient.
2. The following results were achieved in the fields of empirical research
2.1 We conducted regular monitoring and research on development of Russian economy for topics in industry, external trade, budget, as well as macroeconomic, monetary policy and banking topics. We developed scenarios for economic and social development in Russia both in short- and medium-term. Our continuing research was focused on crisis processes in main sectors of Russian economy, development of scenarios including uncertainty factors linked to oil prices and world economic growth, but also to government policy profile (which could be either stabilizing or proactive).
2.2 We made new estimates for impact of import substitution and export growth to net export, the former was found to be a better strategy than the latter. Overall, import substitution forms 72% modelled increase in net exports. Main prospective areas are transport manufacturing, electric machinery, electronics and optics, and chemicals. Import volume was the main factor influencing the potential for net exports.
2.3 We analysed key industrial sector trends and have shown that current crisis is less prononunced than 2009 crisis. Industrial production decreased only 2.5-3.2% against 9% in 2009, profitability and solvency of companies slightly improved and real wages went down. However, intraseectoral differentiation increased, the worst situation is for investment goods sectors.
2.4 We generalized the experience of economic growth stimuli in the countries with inflation targeting regime, and derived the principles of effective central bank-government coordination. We found that the use of monetary policy rules and effective communication with market participants provides for the optimal transparency and flexibility of monetary policy, and creates the mechanisms for real central bank accountability.
2.5 We conducted the analysis of main factors and trends for money market and currency market trends in 2015. We studied the change in rectin of monetary authorities to the change in the economic and financial conditions. Also we assess the effectiveness of balance of payments adaptation to new external conditions under new currency policy of the Bank of Russia.
2.6 We published a number of regular research and outlooks, including “Trends of Russian Economy”, “Trends in the largest economies”, “Technology monitor for Russia and the world”, “Financial indicators”, and a number of regular monthly research reports During the year, we performed reviews of main economic policy actions, including scenarios and forecasts made by Ministry of Economic Development, and developed scenarios for economic and social development in Russia based on the same assumptions. We analyzed Main directions of monetary policy devoting special attention to risks for monetary policy and banking system.
Level of implementation, recommendations on implementation or outcomes of the implementation of the results
Implementation of research goals will allow to add to the quality of socioeconomic forecasting, including those for the purposes of anti-crisis policy. The results may serve as a base for policy advice in preparing large-scale decisions and long-term strategic official documents.
The materials prepared during the realization of the project were accepted by officials in Office of the President, Ministry of Economic Development, Ministry of Industry and Trade, Ministry of Education and Science.