Applied Econometrics || with 100% Errorless Answers.
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Course
Applied Econometrics
Institution
Applied Econometrics
panel data (longitudinal data) correct answers data for multiple entities in which each entity is observed at two or more time periods
cross-sectional data correct answers Cross sectional data means that we have data from many units, at one point in time.
time series data correct answers Time...
Applied Econometrics || with 100% Errorless Answers.
panel data (longitudinal data) correct answers data for multiple entities in which each entity is
observed at two or more time periods
cross-sectional data correct answers Cross sectional data means that we have data from many
units, at one point in time.
time series data correct answers Time series data means that we have data from one unit, over
many points in time.
instrumental variable correct answers An instrumental variable (sometimes called an
"instrument" variable) is a third variable, Z, used in regression analysis when you have
endogenous variables—variables that are influenced by other variables in the model. In other
words, you use it to account for unexpected behavior between variables.
IV Assumptions correct answers 1. It causes variation in the treatment variable (X)
2. It does not have a direct effect on the outcome variable (Y), only indirectly through the
treatment variable(X).
Autoregressive model correct answers In an autoregression model, we forecast the variable of
interest using a linear combination of past values of the variable.
Heteroskedasticity correct answers -Variance of the residual term, or error term, in a regression
model varies widely.
The interpretation of the slope coefficient in the model Yi = β0 + β1 ln (Xi) + ui correct answers
a 1% change in X is associated with a change in Y of 0.01 β1
Observational data correct answers data from an uncontrolled environment
Experimental data correct answers data originated from experiments that can be controlled by the
investigator. In Economics data are very often observational than experimental, which makes it
dicult to establish causal eects.
OLS Estimator correct answers Is derived by minimising the sum of squared residuals
Interpretation of the OLS Slope Estimate (β1) correct answers -The slope estimate is the sample
covariance between X and Y divided by the sample variance of X
- If X and Y are positively correlated, the slope will be positive
-If X and Y are negatively correlated, the slope will be negative
- Only need X to vary in our sample
-i.e. if X changes by ∆X then Yb will change by ∆X.
, Assumption of the Linear Regression Model correct answers 1. The X's are nonstochastic
variables whose values are fixed or the X values are independent of the error term: cov (ui , Xi )
=0
2. The error term has zero expected value: E u i 2 |Xi = 0 for each i
3.The error term has constant variance for all observations (homoscedasticity).
4. The random variables ui are statistically independent (no autocorrelation): E ui uj = 0 for all i
̸= j
5. The number of observations (n) must be greater than the number of parameters to be
estimated.
6- The nature of X: Variability in X values (var(X) different from 0). No outliers.
7. No exact collinearity between the X variables.
8. The error terms are normally distributed
what is R^2 and what properties does it have? correct answers A statistical measure that
represents the proportion of the variance for a dependent variable that's explained by an
independent variable. Properties: 1. Nonnegative 2. Its limits are 0 ≤ R 2 ≤ 1
Multiple Regression assumptions correct answers 1. Linear in the parameters.
2. Fixed X values or X values independent of the error term. Cov (X2i , ui) = cov (X3i , ui) = . . .
= cov (Xki, ui) = 0 3. Zero mean value of the disturbance.
E (ui |X2i , X3i , . . . , Xki) = 0
4. Homoscedasticity or constant variance of ui .
Var (ui) = σ 2 .
5. No autocorrelation between disturbances.
cov (ui , uj) = 0 for i ̸= j
6. The number of observations n must be greater than the number of parameters to be estimated.
7. There must be variation in the values of the X variables. (Exception: You may have ONE
constant term, so you might think of one X variable equal to 1 for all observations.)
8. No exact collinearity between the X variables.
9. No specification bias.
R^2 in multiple regression correct answers -R^2 can never decrease when another independent
variable is added to a regression, and usually will increase
-Because R 2 will usually increase with the number of independent variables, it is not a good
way to compare models
-To account for the fact that R^2 will always increase when K increases, we could use the
adjusted R^2 .
what is Omitted Variable Bias and how is it fulfilled? correct answers The bias that arises in the
OLS estimators when a relevant variable is omitted from the regression.
Fulfilled when
1. X is correlated with the omitted variable.
2. The omitted variable is a determinant of the dependent variable Y.
F test correct answers jointly test multiple hypotheses about our parameters
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