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Товч мэдээллийг харах

dc.contributor.advisor Tsai, Yung-Shun
dc.contributor.author Amarsanaa, Chinguun
dc.contributor.author Bolormaa, Temuulen
dc.date.accessioned 2019-06-24T09:08:34Z
dc.date.available 2019-06-24T09:08:34Z
dc.date.issued 2019-06-24
dc.identifier Бакалавр en_US
dc.identifier.uri http://repository.ufe.edu.mn/handle/8524/1397
dc.description.abstract This paper analyzes relationship between Shadow Economy and GDP (Gross Domestic Product) in various way. It focuses on the case of several countries such as Mongolia, Russia and China during the period of 1991-2015 of about 25 years. Our goal was to find a topic that is not common and complex to do. Of course find a topic that is common is easy to do and there are enough reference files could be found. Even though Shadow economy is the topic that is not commonly chosen as a project and its complex for student project we decided to stay on it and tried to do research on it. Choosing this kind of topic could open our eyes and help younger students to do research on it. Even it will help us to manage something like shadow economy and understand this secret society. The Empirical analysis of the project will define more interesting results. In conclusion, we suppose that Shadow Economy is part of the human society and it cannot be all eliminated. All countries have their own dosage of Shadow Economy. But the main thing is that which level is toxic or nontoxic for economy. Once it cannot be eliminated we believe the right level or small amount of shadow economy is not affecting bad for the economy in other words any country can contain Shadow Economy as their part of economy but they should limit it under the right parameter. For reaching our goal we took our data from the Global Economy web source. And the data shows the relationship between Shadow Economy and GDP. If we look closely, the main data shows the percentage of black market in GDP of the chosen countries. To find the answer, we put our data on EVIEWS and run tests including Granger Causality, Vector Auto Regression, and models such as Regression, Correlation and other statistics. Only percentage of Shadow Economy in GDP will be not enough, so we took 5 variables. In other words, there are 5 different variables that will help us to identify the problem. After the tests we found out some interesting results that are proven on the reality. And 3 countries Mongolia, Russia and China are significantly connected to each other. Affecting from one to another in different ways can tell that. en_US
ife.Мэргэжил.Нэр Менежмент, санхүүгийн
ife.Мэргэжил.Индекс D340400
ife.Зэрэг Бакалавр

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Товч мэдээллийг харах