international econometric
journal
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.
Anatolyev, Stanislav. Basics of quasi- and pseudo-likelihood
theories
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.
Ivanova, Vera. GRP and environmental pollution in Russian
regions: spatial econometric analysis
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.
Anatolyev, Stanislav; Khrapov, Stanislav.
Do spatial structures yield better volatility forecasts?
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.