Deneme Sınavı Soruları

Observing outcomes over five consecutive years.

Comparing outcomes before and after the treatment in a single time period.

Observing outcomes at two time periods, pre-treatment and post-treatment.

Continuous monitoring of outcomes throughout the treatment.

Annual assessment of outcomes post-treatment.

Single equation models are always the preferred method for analyzing economic relationships due to their simplicity.

The use of system equations is unnecessary when analyzing economic variables, as single equation models can handle all types of causality.

Bi-directional causality among economic variables necessitates the use of system equations rather than single equation models for a more accurate analysis.

Economic variables do not exhibit bi-directional causality, making single equation models sufficient for all econometric analyses.

The direction of causality in economic relationships is not important, so single equation models are always adequate.

0.960

0.667

0.857

0.750

0.968

Changes in healthcare legislation.

Introduction of a new educational curriculum.

Impact of minimum wage introduction in New Jersey.

Effects of a new tax law.

Implementation of a new public transportation system.

It ensures that the treatment effect is consistent over different population subgroups.

It guarantees that the treatment effect remains stable over multiple time periods.

It confirms that the observed outcomes for a given treatment status are equivalent to the potential outcomes for that status.

It ensures that the treatment effect is uniformly distributed across all units.

It verifies that the treatment assignment is random and unbiased.

Because it perfectly predicts the dependent variable without any error.

Because it assumes that all variables of interest are observed and included in the model.

Because it does not account for individual-specific effects that are correlated with the regressors.

Because it only uses cross-sectional data, ignoring the time series component.

Because it assumes that the error terms are perfectly correlated with the regressors.

0.032

0.072

0.143

0.333

0.667

It implies that the treatment effect is consistent across different time periods.

It suggests that the observed value of an outcome is equal to the potential outcome if the treatment status is the same as the observed treatment status.

It indicates that the treatment effect must be consistent across different subgroups within the study.

It requires that the treatment assignment must be random.

It assumes that the error terms in the regression model are homoscedastic.

When the dataset is balanced.

When minimizing false negatives is critical.

When the cost of false positives is high.

When there is a need to balance precision and recall.

When incorrectly identifying negatives as positives has significant consequences. 

0.960

0.667

0.857

0.750

0.968

Increasing the sample size.

Improving the degrees of freedom.

Expanding the variability in explanatory variables.

Guaranteeing the accuracy of predictions.

Decreasing the level of multicollinearity among explanatory determinants.

Accuracy

Precision

Recall

Specificity

F1 Score 

In VAR models, all variables are assumed to follow a linear trend over time.

VAR models require all variables to be exogenously determined before analysis.

In VAR models, all the variables are assumed as endogenous.

VAR models exclusively use stationary variables without requiring any differencing.

In VAR models, variables are classified into dependent and independent variables for analysis.

Only I

Only II

I and II

I and III

II and III

The number of autoregressive terms

The number of moving average terms

The order of the autoregressive component

The order of the moving average component

The number of times the series needs to be differenced to achieve stationarity

Errors in Variables

Simultaneous Equation

Sample Selection Problem

Endogeneity Bias

Endogeneity Problem

The model becomes non-linear in parameters.

The t and F statistics are no longer t and F distributed.

The bias in the standard errors are reliable for hypothesis testing and confidence intervals.

The LM statistic has an asymptotic chi-square distribution.

The OLS estimator can still be efficient under heteroskedasticity.

MA(1)

AR(1)

AR(2)

MA(2)

AR(3)

It implies that certain variables within the model are determined within the system itself.

It implies that certain variables within the model are determined outside the system itself.

It implies that certain variables within the model are not necessarily determined.

It implies that certain variables within the model are constant in the system itself.

It implies that certain variables outside the model are determined within the system itself.