Single equation models such as regression models have one dependent and one or more independent variables.
Econometrıcs II (ENG) — Ünite 5 Soru-Cevap
Econometrıcs II (ENG) (IKT326U) soru-cevapları.
Which indicators do regression models contain?
What is main problem of simultaneous equations?
Main problem of the simultaneous equations is to determine the exogenous and endogenous
variables in the model.
What are the endogenous variables defined as?
The endogenous variables are defined as the variables that mutually affect each other within the model.
What are the exogenous variables defined as?
The exogenous variables are defined as the variables which are not identified within the model.
How can the exogenous variable(s) be determined?
If a variable is present only at the right side of the models in simultaneous equations, we can identify that variable as exogenous.
How can the endogenous variable(s) be determined?
If a variable is at the left side of the models in simultaneous equations, we can determine that the variable is endogenous
What are the specialities of Vector Autoregression (VAR) models?
The VAR models are linear and in VAR models, all the variables are assumed as endogenous.
Which model was created as an alternative model to the simultaneous equations?
Sims (1980) pointed out the exogeneity assumptions for some of the variables in the simultaneous equations and proposed the Vector Autoregression (VAR) model as an alternative model to the simultaneous equations.
Which method is used to estimate VAR model?
We estimate VAR models using the ordinary least squares (OLS) method.
Although the normality assumption is expected to be held, are which tests also essential to employ for VAR models?
Although the normality assumption is expected to be held, it is not a necessary condition for VAR models. It is known that the normality assumption is essential for employing the t-tests and F-tests in econometric models.
What is purpose of multivariate tests of normality assumption in the VAR model?
The multivariate tests of normality assumption can be used to check whether the third and fourth moments of the residuals are appropriate with a normal distribution.
In which situation of Jarque- Bera test, the null hypothesis of normality cannot be rejected?
If the Jarque-Bera test statistic is smaller than the χ2 critical value, the null hypothesis of normality cannot be rejected.
For the stability of the VAR model, in which part of unit circle must the inverse roots take part?
The inverse roots of the AR characteristic polynomial are inside the unit circle so the VAR(2) model satisfies the stability condition.
How is the Granger causality test applied?
The Granger causality test is applied by restricting the parameters of the VAR model.
In which situation of causality test, the null hypothesis of non-causality will be rejected?
If the calculated Chi-Square test statistic is greater than χ2, the critical value with p degree of freedom, the null hypothesis of non-causality is rejected.
Why is essential to predict responses of variables to changes in the other variables?
The predicting responses of variables to changes in the other variables are essential in terms of policy shocks for the economic agents.
What are advantages of impulse-response analysis in the VAR model?
They are also useful in understanding the dynamic behavior of the variables in the VAR model and provide the prediction of the influences of shocks and policy analysis.
How many times should impulse response functions cut the zero line at least?
The impulse response functions should cut the zero line at least one time.
What is the role of variance decomposition analysis?
Variance decomposition analysis breaks down the variance of the forecast error into components that can be attributed to each of the endogenous variables.
For the correct ordering in the variance decomposition analysis, what should be considered?
For the correct ordering, we consider their own variances of forecast error for all variables in the first period and then we order the variables from the greatest variance value to the smallest one.