A final year BSc Finance student estimated a financial econometric model of the form Yt = α + β1X1t + β2X2t + β3X3t + β4X4t + ut, where; ? ? is stock market development proxied by stock market capitalisation, ?1? is real gross domestic product growth rate, ?2? is stock market liquidity proxied by market value traded, ?3? is banking sector development proxied by domestic credit, ?4? is democratic accountability, ?, ?1, ?2, ?3, ??? ?4, are all parameters to be estimated, and ?? is the unobservable error term. He subsequently estimated specified the model and reported the results as follows (where p-values are in parentheses and standard errors are in squared brackets): Yt = 0.68 + 0.29X1t + 0.15X2t + 0.19X3t + 0.31X4t (0.000) (0.000) (0.001) (0.002) (0.000) [0.090] [0.072] [0.016] [0.054] [0.121] R-square 0.862495 F-statistic 2.944376 (0.000953) Explain why you think the model is a well specified financial econometric model. Outline the five assumptions that you have to make about the unobservable error term under the classical linear regression model. Interpret the results in terms of the parameters estimated (be very brief). Explain briefly whether the explanatory variables are individually and jointly statistically significant at the 5%. Outline three diagnostic tests you should perform about the residuals.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
QUESTION 3:
A final year BSc Finance student estimated a financial econometric model of the form Yt = α + β1X1t + β2X2t + β3X3t + β4X4t + ut, where; ? ? is stock market development proxied by stock market capitalisation, ?1? is real gross domestic product growth rate, ?2? is stock market liquidity proxied by market value traded, ?3? is banking sector development proxied by domestic credit, ?4? is democratic accountability, ?, ?1, ?2, ?3, ??? ?4, are all parameters to be estimated, and ?? is the unobservable error term.
He subsequently estimated specified the model and reported the results as follows (where p-values are in parentheses and standard errors are in squared brackets):
Yt = 0.68 + 0.29X1t + 0.15X2t + 0.19X3t + 0.31X4t
(0.000) (0.000) (0.001) (0.002) (0.000)
[0.090] [0.072] [0.016] [0.054] [0.121]
R-square 0.862495 F-statistic 2.944376 (0.000953)
- Explain why you think the model is a well specified financial econometric model.
- Outline the five assumptions that you have to make about the unobservable error term under the classical linear regression model.
- Interpret the results in terms of the parameters estimated (be very brief).
- Explain briefly whether the explanatory variables are individually and jointly statistically significant at the 5%.
- Outline three diagnostic tests you should perform about the residuals.
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