Quantitative variables can easily be represented by numbers.
Econometrıcs I (ENG) — Ünite 5 Soru-Cevap
Econometrıcs I (ENG) (IKT325U) soru-cevapları.
How quantitative variables can be represented in regression model?
What are examples of quantitative variables?
Consumption, wage, investment expenditure, unit production, cost, exam grade, weight and age are examples of quantitative variables.
What is dummy variable?
New variable which takes numerical values must be defined and its values must be able to represent something unnumerical. A variable defined for this purpose is called a Dummy Variable
What do dummy variable represent in linear regression?
Dummy variable which represents the qualitative data with numerical values.
What is wrong for quantitative variables?
They can be used as independent variables but cannot be used as dependent variables
What Di is used for in linear regression model?
Di is used for qualitative explanatory variables.
What is base group in linear regression?
The group in which the dummy variable takes the value zero is called the base group.
How do represent 3 variable by dummy in regression model?
To represent three different states of the qualitative variable, three separate dummy variables are defined as binary variables each of which takes the value of either 0 or 1.
For example;
First dummy variable (for science field): D1i
D1i =1 student graduated from science field
D1i =0 student graduated from other than science field
Second dummy variable (for Turkish-math field): D2i
D2i
=1 student graduated from Turkish-math field
D2i
=0 student graduated from other than Turkish-math field
Third dummy variable (for social science field): D3i
D3i
=1 student graduated from social science field
D3i
=0 student graduated from other than social science field
What is Dummy Variable Trap?
If all dummy variables were added to the model without excluding one of the dummy variables, a “Perfect Multicollinearity” problem would arise and the β coefficients of 5.5 would not be calculated. This situation is called Dummy Variable Trap.
How do avoid dummy variables trap?
To avoid dummy variables trap, as a general rule, if a qualitative variable represents n
states, n-1 dummy variables should be used in the regression model.
What is called dummy variables take the value of zero?
Dummy variables take the value of zero is called the base group.
What do the coefficients of the dummy variables express?
The coefficients of the dummy variables express the difference between the group of the related dummy variable and the base group.
When is necessary to consider the interactions among dummy variables?
In situations where the effects of two distinct qualitative variables are investigated, it is necessary to consider the interactions among dummy variables.
What should be added to model in order to representing two different qualitative variables?
A new variable representing the cross states of two different dummy variables representing two different qualitative variables needs to be added to the model.
What can be easily tested by using dummy variables in linear regression models?
By using dummy variables, linear regression models can be easily tested to see whether there is a structural change in the model.
What may be the structural differences across various time periods or groups in econometric model?
Econometric models may have structural differences across various time periods or groups. For example, the structure of the function between “study time” and “final exam grade” may be different for two student groups, students with a computer and students without a computer.
In the context of the Linear Probability Model (LPM) , which of the statements is wrong?
LPM always produces probabilities that are between 0 and 1.
"If the dependent variable on the left hand side of the linear regression model is a dummy variable, then the conditional expected value of the dependent variable is equal to the probability of the dependent variable being one."
How this equality can be illustrated mathematically?
E(Yi | Xi) = P (Yi = 1| Xi) = β0+β1Xi
What are the most important points to be cautious about if a dummy variable is used as the dependent variable in a linear regression model?
The most important of these points is that since the regression model explain the probability values of a dependent variable being one, the predicted value generated by the right-hand side must be in the interval between zero and one.
Using dummy variables instead of whatever statistical method offers a very practical way to test for differences?
Instead of Chow Statistics, the use of dummy variables offers a very practical method to test for differences in linear regression models for different states of quantitative variables.