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Modeling of Taylor rule for monetary policy in Russia: empirical analysis

Fedorova E.A. Doctor of Economic Sciences, Associate Professor, the Department of Financial Management, the Financial University under the Government of the Russian Federation ( ecolena@mail.ru )

Lysenkova A.V. Applicant of the Department of Financial Management, the Financial University under the Government of the Russian Federation ( lysenkovaav@gmail.com )

Journal: Finance and Credit, #37, 2013

In the article by means of econometric modeling (model with Markov switching) research of a credit policy of the Russian Federation during the period from 2001 to 2011 on the basis of Taylor's rule is made. The assessment of policy of Bank of Russia with allocation of priorities of monetary policy is carried also out. New modification of the rule of Taylor, considering features of economy of the Russian Federation is offered.


On a certain approach to comparing the regions in different countries based on estimated technical efficiency of innovative space

Afanas'ev M.Yu. Central Economics and Mathematics Institute of RAS (CEMI RAS), Moscow, Russian Federation ( miafan@cemi.rssi.ru )

Kudrov A.V. Central Economics and Mathematics Institute of RAS (CEMI RAS), Moscow, Russian Federation ( kovlal@inbox.ru )

Lysenkova M.A. Central Economics and Mathematics Institute of RAS (CEMI RAS), Moscow, Russian Federation ( lysenkovam@gmail.com )

Journal: Economic Analysis: Theory and Practice, #9, 2019

Subject We assess the technical efficiency of innovative space in regions of different countries.
Objectives The purpose is to get comparable estimates of technical efficiency of innovative space in regions of different countries that retain the rank of regions formed according to ‘national models’.
Methods The study rests on the concept of a stochastic frontier and an optimization model with a quadratic target function and linear limitations.
Results We offer an approach enabling to adjust the technical efficiency estimates obtained from the model, which is common to the entire population of regions, so that they retain the rank of regions formed according to ‘national’ models. This approach provides comparable technical performance assessments in a wide range of tasks to model the limits of production capacity of economic facilities that are operating in different institutional environment.
Conclusions A natural way to obtain comparable technical performance estimates is to build a common model for the entire population of regions. However, the results of the general model seldom have a satisfactory economic interpretation, as the innovation activities of regions of different countries is conditioned by different institutional environment. Using the offered model, the estimates derived from the general model are adjusted so that their grades are fully in line with the estimates received from the so called ‘national’ models.


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