Deneme Sınavı Soruları

By limiting the sample size.

By increasing multicollinearity among explanatory determinants.

By reducing the variability in explanatory variables.

By expanding the sample size and improving the degrees of freedom.

By focusing solely on cross-sectional data.

They vary significantly across individuals and time.

They remain constant across time but vary across individuals.

They remain the same throughout time and space.

Only the slope coefficients are assumed to be constant.

Intercept coefficients are variable, while slope coefficients are constant.

0.960

0.667

0.857

0.750

0.968

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. 

Simultaneity is a phenomenon where variables occur at the same time but has no impact on the concept of endogeneity in regression analysis.

Simultaneity is a form of endogeneity that arises when an explanatory variable is correlated with the error term, violating the assumption of exogeneity in classical linear regression models.

Endogeneity only occurs due to omitted variable bias and measurement errors, and simultaneity is a separate issue that does not affect regression models.

In classical linear regression models, explanatory variables are assumed to be endogenous, which means simultaneity is a condition that is always met.

Simultaneity and endogeneity are completely unrelated concepts; simultaneity is a technique used to ensure variables are exogenous.

A decline in the application of econometric approaches to panel data.

The establishment of panel unit root tests, panel co-integration studies, and panel Granger causality tests.

A complete shift away from using panel data in applied econometric studies.

The elimination of the need for panel data models.

A focus on using panel data exclusively for microeconometric analysis.

To adjust for inflation over different time periods.

To observe the outcomes of treatment and control groups at multiple time points.

To compare the outcomes of different geographical locations over time.

To measure the variability of outcomes within the same group.

To ensure that the treatment effects are consistent across different subgroups.

The relationship between a country's GDP growth rate and its population growth rate, where neither variable directly influences the other.

The scenario where an increase in consumer confidence leads to higher spending, which in turn boosts economic growth, without any feedback effect from economic growth to consumer confidence.

The relationship between inflation and unemployment, where an increase in inflation might decrease unemployment due to higher demand for goods (short-term Phillips curve effect), and changes in unemployment can influence inflation through wage pressures.

The effect of weather conditions on crop yields, where only the weather conditions directly influence the crop yields without any feedback mechanism.

The situation where changes in a company's operational efficiency directly lead to variations in its stock price, with no influence of the stock price on the company's operational decisions.

It represents the average treatment effect on the treated, comparing the expected outcomes of the treated group before and after the treatment to the control group's outcomes in the same periods. 

It denotes the average treatment effect for the entire population, not just the treated.

It indicates the difference in outcomes within the treated group only, without comparison to any control group. 

It is a formula used to calculate the variance of the treatment effect. 

It represents the interaction effect of treatment and time in a regression model.

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.

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.

They cannot handle large datasets.

They always require time series data.

They are frequently gathered from surveys, which can introduce various biases.

They can only be used for microeconometric analysis.

They eliminate the need for econometric estimators.

Accuracy

Precision

Recall

Specificity

F1 Score 

Only I

Only II

I and II

I and III

II and III

0.1

0.3

0.5

0.4

0.6

Y(t)=0.5-2t

Y(t)=2+8t

Y(t)=2-t

Y(t)=-2t

Y(t)=-1+2t

The choice of base period.

The number of items in the basket.

The consumption quantities used as weights.

The interpretation of index values.

The calculation method.

Stationary process

Autocorrelation function (ACF)

Partial autocorrelation function (PACF)

White noise processes

Trend analysis