This paper considers the economic returns to health in the Russian labor mar-ket. Empirical analysis the impact of health on labor supply and wages is provided on the base of RLMS data for 1994-2004. The problem of measuring health and dif-ferent of subjective and objective health characteristics are discussed. The problem of health endogeneity is concerned. The results of estimation show considerable negative influence of poor health and relatively less in absolute value positive influence of good health on employment. We also have found a positive impact of good health on earnings.
Proposed a model of financial bubbles and crises based upon the methodology of complex systems analysis. It was shown how the procedures (slice and dice) of a CDO synthesis generated the excess growth of the securitized assets value. The latter being coupled with the high leverage might produce the total collapse of a financial system. On a macrolevel of a system its behaviour was modeled by a differential equation depending on three parameters. The irrationality of financial investors, as it was well known, had been empirically explained by «the greater fool theory». This process, in modern terms, was represented as the autocatalytic process leading to a system's singularity. Such an outcome was explained on the system's microlevel as a process of financial percolation which was modeled, quite surprisingly, by the same equation of a Bernoulli type. Invariant constants of percolation were used to estimate different parameters of a model. The model application to the study of 2007-2010 credit crunch has given rise to the impressively coherent results in terms of probabilities and the return time periods of critical events that took place on the global financial markets.
Proposed a model of financial bubbles and crises based upon the methodology of complex systems analysis. The irrationality of financial investors, as it was well known, had been empirically explained by «the greater fool theory». This process, in modern terms, was represented as the autocatalytic process leading to a system's singularity. It was shown how the procedures (slice and dice) of a CDO synthesis generated the excess growth of the securitized assets value. The latter being coupled with the high le-verage might produce the total collapse of a financial system. On a macrolevel the behaviour the of a system was modeled by a differential equation depending on three parameters. Such an outcome was explained on the system's microlevel as a process of financial percolation which was modeled, quite surprisingly, by the same equation of a Bernoulli type. Invariant constants of percolation were used to estimate different parameters of a model. The model application to the study of 2007-2010 credit crunch has given rise to the impressively coherent results in terms of probabilities and the return time periods of critical events that took place on the global financial markets.
This paper compares the forecasting performance of random walk, frequentist vector autoregression (VAR), and Bayesian vector autoregression with Minnesota prior (BVAR) models on quarterly Russian data sample running from 1995 to 2014. Maximal number of variables included in the model is equal to 14 that requires an endogenous search of optimal shrinkage hyperparameter. The search procedure follows [Bańbura et al., 2010] and [Bloor and Matheson, 2011]. According to the selection method the shrinkage hyperparameter equates the forecasting quality of the frequentist VAR and BVAR for the minimal considered dimension of the model (three variables). For any dimension of the BVAR model the optimal shrinkage hyperparameter is robust to considered functions of relative forecasting accuracy.
We show that the BVAR provides a more accurate forecast than the frequentist VAR on the studied sample. For key macro indicators (the industrial production index, consumer price index and the interbank interest rate), forecasting horizons, and all model sizes, the mean squared error of the BVAR is lower than that of the frequentist VAR. Moreover, the results show that the forecast made using the BVAR is more precise than the forecast made using random walk model for the CPI and using white noise model for the interbank rate. However, the BVAR cannot beat the random walk while forecasting the industrial production index.
This paper discusses the problems of modeling efficiency of firms. There are two the most popular methods to estimate efficiency of firms: DEA (data envelopment analysis) and SFA (stochastic frontier analysis), and popularity of the last one is fast growing. There are a lot of different SFA models, so most researches often choose in advance one or two models, which they are going to estimate. So survey of different SFA models is one of goals of this paper. We discuss 15 popular SFA models. Also we discuss problems of SFA models and their prospects. In our paper we compare models, estimated by classical method of moments (MoM), and models, estimated by maximum likelihood approach (MML). Today there are no such papers, so we try to discuss pros and cons of using method of moments approach in SFA models. Interesting, that this method is very unpopular today, but its’ estimates are asymptotical normal and consistent. Because there are no formal criteria to compare different SFA models, we investigate the estimation results from 9 SFA models on the concrete industry data. We use correlation analysis of estimates of efficiency ranks and also we try to find out the causes of the most serious differences between models.
We conduct a statistical study of the global trade slowdown relative to industrial production after the global financial crisis of 2008-2009 and the revival of the global trade in 2017. We aim to decompose the overall effect by geographical and commodity dimensions, that is, to determine the contribution of regions, major countries and aggregated commodity groups to the global trade slowdown and revival. Calculation scheme implies the two-way analysis, both from the demand and the supply side (imports and exports, respectively), and relies on the customs data. The focus on merchandise trade is confirmed by the fact that the growth rates of world imports and exports in constant prices for services decreased much less after the crisis of 2008-2009 than for goods. We analyze the dynamics of world trade relative to the dynamics of industrial production, not GDP, due to the very high volatility of trade to GDP. The data comes from the Netherlands Bureau for Economic Policy Analysis, WTO, World Bank, OECD, FAO and the Chatham House Resource Trade Database. The key feature of the proposed scheme is aligning data on international trade growth for largest countries from different sources with CPB data for the world as a whole.
The results of the study show that the slowdown in global exports was largely associated with emerging economies of Asia (and, primarily, China), Japan, Germany and the US, while the slowdown in global imports reflected the drop in demand in China and other emerging economies of Asia, the Euro Area, Russia and Brazil. The revival of global exports was driven by China, Japan, Netherlands, South Korea and Mexico, and the revival of world imports boils down to the demand growth in China, India and Russia. Unlike the global trade slowdown, the revival of the world trade was critically concentrated in emerging countries, while the Euro Area has practically not experienced the revival. An important role in the global trade slowdown was played by China and its reorientation to the domestic market after the crisis of 2008-2009. In terms of commodity structure of global trade, the slowdown was almost entirely associated with non-resource goods.
The results can be used to refine the forecasts of the global merchandise trade growth by accounting for the contribution of the major countries more accurately.
In this paper we propose and implement a mechanism of modeling the price indices of food purchases by income groups of households. These indices could be interpreted as differentiated by income food inflation. This approach is based on the differences in prices of purchases for the income groups within each year. We provide the calculations of these indices for the RLMS data and Households Budget Survey conducted by Rosstat (HBS). We discuss possible modifications of the proposed procedure for goals of forecasting of inflation differentiated by income groups. In the result of the comparison with direct calculation of inflation separately for each income group we conclude that the proposed in the paper approach has several advantages, including lower requirements of amount of incoming information.