in Russian language
The past half-century has seen economic research become increasingly empirical, while the nature of empirical economic research has also changed. In the 1960s and 1970s, an empirical economist's typical mission was to “explain” economic variables like wages or GDP growth. Applied econometrics has since evolved to prioritize the estimation of specific causal effects and empirical policy analysis over general models of outcome determination. Yet econometric instruction remains mostly abstract, focusing on the search for “true models” and technical concerns associated with classical regression assumptions. Questions of research design and causality still take a back seat in the classroom, in spite of having risen to the top of the modern empirical agenda. This essay traces the divergent development of econometric teaching and empirical practice, arguing for a pedagogical paradigm shift.
This study is aimed to describe the current state of affairs in teaching econometrics and applied economics in Russian regional universities. It is based on a survey of regional university teachers who were enrolled in training programs of the Yegor Gaidar Foundation in 2017–2019. This survey included questions related to the participants’ teaching experience as well as their own experience in studying econometrics that they had had before the program and their research experience. Participants were also asked to formulate what they had learned during the program. The analysis of answers received confirms the well-known opinion that regional universities are lagging behind leading metropolitan universities in training and qualification in the field of applied econometrics/economics, but this lag looks neither crucial nor chronic.
Statistics is one of basic ingredients in training of economists. It acts as a tool of cognition, as well as accumulates the experience of empirical research. During the educational process, the components of statistics are gradually studied and used at all stages of the educational process. The article considers the content and interrelation of statistical disciplines at various stages of training of economists. Special attention is paid to the relationship of applied statistical analysis and econometrics. We consider interaction among elements of educational programs that allows to ensure harmonious construction of the educational process of training of economists well acquainted with statistical tools, the methodology of statistical research, and modern information technologies that are necessary for analytical work.
This essay contains a brief review of concepts and methods related to the principle of maximum likelihood based on misspecified distributions: quasi-density, pseudo-density, quasi-likelihood, pseudo-likelihood, etc. The review is accompanied with examples and problems.
The article performs empirical estimation of the relationship between per capita income and per capita pollutant emissions in Russian regions taking into account their spatial interdependence. It is shown that the pollutant emissions in the Russian regions are spatially autocorrelated. The estimation results confirm an inverted U-shaped relationship between per capita income and per capita pollution at the regional level. The estimates of the income turning point suggest that most Russian regions are on an upward part of the environmental Kuznets curve, i.e., an increase in GRP is associated with higher pollution levels.
We evaluate, using forecasting experiments with real stock return data, forecasting ability of spatially structured BEKK specifications relative to standard BEKK. We confirm that the class of spatial BEKK has a potential of improving a quality of multivariate volatility forecasts. However, there is a sharp disagreement among forecast performance criteria on which types of further restrictions on coefficient matrices are most promising, on which degree of homogeneity of matrix coefficients is most beneficial, and on which grouping criteria and their number deliver highest improvements in volatility forecasts. The numerosity and composition of the portfolio also have a big influence on how well volatility is forecast by spatially structured BEKK compared to its standard configuration.
The article is devoted to estimation of volatility spillovers in the oil and gas market accounting for cross-sectional dependence. We use data on daily stock returns of 67 companies from the oil and gas sector from 13 countries. The volatility spillovers are estimated via a spatial specification of the BEKK model. Using the Vuong test, we compare explanatory power of the spatial BEKK and non-spatial GO-GARCH and ADCC models, the Diebold-Mariano and Hansen-Lunde-Nason tests being used for evaluating the predictive ability. The Vuong test reveals equal explanatory ability of the three models at any reasonable significance level. In the out-of-sample comparison, the tests do not provide clear evidence of significant superiority of the spatial specification over the other models.