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Publicēts: 29.05.2015.
Valoda: Angļu
Līmenis: Augstskolas
Literatūras saraksts: Nav
Atsauces: Ir
Nr. Sadaļas nosaukums  Lpp.
1.  Introduction    4
2.  Literature review    5
2.1.  Opinions on the connection    5
2.2.  Measuring the influence    7
2.3.  The significance of the stock market’s development    9
2.4.  Measuring the stock market development    10
2.5.  Equity issues vs. debt    11
3.  Methodology    13
3.1.  Data    13
3.2.  Regression model    14
4.1.  Market capitalization to GDP    16
4.2.  Number of listed companies per capita    17
5.  The model    18
5.1.  Limitations    18
5.2.  Correlation between Gross Domestic Product (GDP) and German Stock Index Deutscher Aktienindex (DAX)    19
5.3.  Regression causality    20
5.4.  Explanatory variables    22
5.5.  Two Stage Least Square    23
5.6.  Determining the potential Instrumental Variables    24
5.7.  Instrumental Variables    25
6.  Analysis    26
6.1.  Time series linear regression analysis    26
6.2.  Instrumental variable regression analysis    28
7.  The stock market in Latvia    29
7.1.  The current situation    29
7.2.  How has the Latvian stock market developed over the time?    31
7.3.  Possible developments in the future    33
8.  Conclusion    35
  Appendices    36
  References    39
Darba fragmentsAizvērt

8. Conclusion
After creating the Stata analysis, the authors arrived to several conclusions. To begin with, there is no doubt that the stock market and GDP move together by a certain amount and an approximate amount is 0.013% of an additional increase in GDP with an additional increase by 1% in DAX. However, this result cannot be interpreted lightly due to the presence of reverse causality, since stock markets are closely linked to the economy, as proven by the Granger test in Appendix 6. Separate assumptions were made as to why some macroeconomic variables did not pass at a 10% significance level. There are spikes in these variables that the economic system takes time to adjust, or perhaps they do not do so at all, because of off-setting situations, for example. Some variables do not change that much over time, which opens the door for the question – whether or not that particular variable that slowly changes really has any long term effect on GDP?
When evaluating the results using TSLS and two IV estimators, dividend yields and Ifo, the results were abnormally biased, with the exception of DAX. Both of them were tested separately as well as together, yet, yielding similar outcomes. These results should not be interpreted as indicators for true predictive numbers. Thus, the authors assume that reverse causality does not hold in order to achieve close estimates of the true values from a regular time series linear regression model. It is comprehendible that from this type of regression the numbers are biased to some extent, yet it allows to base the authors’ analysis to some point.
In order to perform a further research on this topic, the model presented in this paper can be used as a valuable tool, however, the most important task would be finding an appropriate instrumental variable, thus achieving less biased results.

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