Question 1

Panel data differs from time-series data in that panel data is observed
A)for a number of different individuals in a number of different time-periods.
B)for a given individual in a number of different time-periods.
C)for a number of different individuals in a given time-period.
D)for a given individual in a given time-period.

Accepted Answer

Panel data is characterized by observing multiple subjects (individuals, firms, countries, etc.) across multiple time periods, allowing researchers to analyze both cross-sectional and time-series variations.

Question 2

First-differenced models are preferable to pooled cross-section models because they
A)control for potential autocorrelation.
B)control for potential heteroskedasticity.
C)control for the random effects in the data.
D)remove the time constant component in the error term.

Accepted Answer

First-differenced models remove the time constant component in the error term by differencing the data over time, which is not achieved in pooled cross-section models. First-differencing does not necessarily control for autocorrelation or heteroskedasticity, and it may still be necessary to address these issues separately. Controlling for random effects in the data is more commonly achieved through fixed or random effects models, rather than first-differenced models.

Question 3

The fact that panel data varies across both individuals and time-periods should allow us to ___________ by accounting for the time constant component in the error term.
A)generate unbiased parameter estimates
B)correctly estimate the true parameter values
C)control for autocorrelation
D)improve the efficiency of the parameter estimates

Accepted Answer

D

Question 4

If there is a pooled cross-section model with and intercept and five explanatory variables and there are 100 observations each year for three years
A)the sample size is 100.
B)there are 294 degrees of freedom.
C)the sample size is 294.
D)there are 300 degrees of freedom.

Accepted Answer

The sample size is 300 (100 observations each year for three years). With five explanatory variables plus an intercept, there are six parameters to estimate. Degrees of freedom are calculated as the sample size minus the number of parameters estimated: 300 - 6 = 294.

Question 5

Fixed-effects models are more appropriate than random-effects models when
A)the error terms are autocorrelated.
B)the independent variables are constant over time.
C)the time invariant component of the error term is correlated with any of the independent variables.
D)the error term is not constant.

Accepted Answer

Fixed-effects models are more appropriate than random-effects models when the time invariant component of the error term is correlated with any of the independent variables. This is because random-effects models assume that the error term is uncorrelated with the independent variables, which may not be appropriate if there is a fixed effect that is correlated with the independent variables. The other options do not necessarily require the use of fixed-effects models over random-effects models.

Question 6

One can first-difference panel data in a two-period model by subtracting
A)the previous-period observation from the current-period observation for the dependent variable.
B)the previous-period observation from the current-period observation for the dependent variable and each independent variable.
C)the current-period observation from the previous-period observation for the dependent variable and each independent variable.
D)the current-period observation from the previous-period observation for the independent variable.

Accepted Answer

First-differencing in panel data involves subtracting the previous-period observation from the current-period observation for both the dependent variable and each independent variable. This method is used to eliminate individual-specific effects that are constant over time.

Question 7

Fixed-effects models
A)remove the time-variant component of the error term.
B)remove the time-invariant component of the error term.
C)work by first-differencing the data.
D)remove the heteroskedasticity in the data.

Accepted Answer

Fixed-effects models remove the time-invariant component of the error term, also known as individual-specific effects, by including individual-specific dummy variables in the regression equation. This allows us to isolate the effect of time-varying independent variables on the outcome variable. It does not remove heteroskedasticity or involve first-differencing the data.

Question 8

Pooled cross-section models
A)control for individual fixed-effects.
B)combine observations over many different years.
C)control for unobserved heterogeneity.
D)control for random-effects.

Accepted Answer

Pooled cross-section models combine data from different time periods or groups to increase the sample size and improve statistical power, but they do not inherently control for individual fixed effects, unobserved heterogeneity, or random effects without additional specifications.

Question 9

Treating each data point in panel data as an individual observation and not accounting for the fact that individuals are observed over many time periods is referred to as estimating a ____ model.
A)static time-series
B)heteroskedastic consistent
C)pooled cross-section
D)fixed-effects

Accepted Answer

Pooled cross-section model treats each data point as an individual observation without taking into account the fact that individuals are observed over multiple time periods, hence it is the best choice for this scenario. Static time-series models only use time series data and do not account for cross-sectional differences, heteroskedastic consistent models are used to correct for heteroskedasticity, and fixed-effects models account for time-invariant individual differences.

Question 10

Fixed-effects models are preferable to pooled cross-section models because they
A)first-difference the data.
B)control for potential heteroskedasticity.
C)control for autocorrelation.
D)remove the time-invariant component of the error term.

Accepted Answer

Fixed-effects models remove the time-invariant component of the error term by including dummy variables for each individual in the sample. This allows for individual heterogeneity and other unobserved variables to be controlled for within the model, resulting in more accurate estimates of the coefficients of interest. Pooled cross-section models, on the other hand, do not control for such individual fixed effects and may suffer from omitted variable bias.

Question 11

Fixed-effects models and first-differenced models
A)assume that the time-invariant component of the error term is not correlated with the independent variables.
B)both add dummy variables to control for individual observations.
C)do not provide identical results if there are more than two years of data.
D)do not completely remove the time-invariant component of the error term.

Accepted Answer

Fixed-effects models and first-differenced models do not provide identical results if there are more than two years of data because they handle the time-invariant component differently, leading to different estimates.

Question 12

Random-effects models improve on pooled cross-section models because they
A)do not take advantage of the panel data nature of the data.
B)have more degrees of freedom available.
C)improve the efficiency of the estimated coefficients.
D)correct for heteroskedasticity.

Accepted Answer

Random-effects models improve the efficiency of the estimated coefficients by allowing for individual-level variation in the intercept term, while still controlling for time-invariant unobserved factors that affect all individuals in the sample. This is not achieved by pooled cross-section models, which assume a common intercept and slope across all individuals and observations. The availability of more degrees of freedom is not a defining feature of random-effects models, and they may also be used to correct for heteroskedasticity, but this is not their primary advantage over pooled cross-section models.

Question 13

Pooled cross-section models are not the preferred estimators for panel data models because they
A)are heteroskedastic.
B)are quasi-differenced.
C)do nothing to account for the time constant component of the error term.
D)create a dummy variable for each individual in the data set.

Accepted Answer

Pooled cross-section models do not account for the time constant component of the error term, which can affect the accuracy of the estimates. Panel data models, such as random effects or fixed effects models, are the preferred estimators for this reason. Pooled cross-section models are also vulnerable to heteroskedasticity, which can lead to biased standard errors and inconsistent parameter estimates. Quasi-differenced models are sometimes used as an alternative to pooled cross-section models, but they are not as robust as panel data models. Dummy variable regression models are another option for analyzing panel data, but they may not be feasible if the number of individuals in the panel is too large.

Question 14

Random-effects models are more appropriate than fixed-effects models when
A)the time invariant component of the error term is not correlated with one or more the independent variables.
B)dealing with panel data.
C)the error term is random.
D)the intercept is not constant.

Accepted Answer

Random-effects models are suitable when the time-invariant component of the error term is not correlated with the independent variables, allowing for more efficient estimations compared to fixed-effects models.

Question 15

Fixed-effects models and first-differenced models
A)assume that the time-invariant component of the error term is not correlated with the independent variables.
B)both add dummy variables to control for individual observations.
C)control for unobserved heterogeneity.
D)do not completely remove the time-invariant component of the error term.

Accepted Answer

Both fixed-effects models and first-differenced models control for unobserved heterogeneity, which is often a concern in panel data analysis. However, fixed-effects models do not completely remove the time-invariant component of the error term, which may still bias the estimate of the coefficients. First-differenced models, on the other hand, remove the time-invariant component but may suffer from a loss of information due to the differencing process. Both models add dummy variables to control for individual observations, but the key difference lies in their treatment of the time-invariant component of the error term. Therefore, the best choice in this case is C.

Question 16

Random-effects models are
A)almost never the appropriate model in economics because the unobserved heterogeneity is typically correlated with the independent variables.
B)typically more appropriate than fixed effects models in economics.
C)preferred when the error term is random.
D)difficult to interpret because the coefficient estimates are not true marginal effects.

Accepted Answer

Random-effects models are often inappropriate in economics because unobserved heterogeneity is usually correlated with the independent variables, violating the assumption of random effects.

Question 17

Fixed-effects models and first-differenced models
A)assume that the time-invariant component of the error term is not correlated with the independent variables.
B)both add dummy variables to control for individual observations.
C)provide identical results if there are only two years of data.
D)do not completely remove the time-invariant component of the error term.

Accepted Answer

Fixed-effects models assume that the time-invariant component of the error term is not correlated with the independent variables, whereas first-differenced models do not make this assumption. Therefore, the best choice is A. Fixed-effects models and first-differenced models both add dummy variables to control for individual observations, but their underlying assumptions and methodology differ. Additionally, while first-differenced models eliminate the time-invariant component of the error term, fixed-effects models only partially remove it. Lastly, fixed-effects models and first-differenced models provide similar results if there are only two years of data, but they may differ if there are more observations available.

Question 18

Random-effects models
A)assume that the time invariant component of the error term is not correlated with the independent variables.
B)first difference the data.
C)are impossible to estimate in STATA.
D)can only be estimated if the panel data only have two time periods.

Accepted Answer

The answer of Random-effects models
A)assume that the time invariant component...

Question 19

The biggest difference between fixed-effects and random-effects models is that
A)fixed effects models assume that the unobserved heteroskedasticity is uncorrelated with the independent variables.
B)random effects models do not completely remove the time-invariant component of the error term.
C)fixed effects models completely remove the time-variant component of the error term.
D)random effects models are random while fixed-effects models are fixed.

Accepted Answer

Random effects models do not completely remove the time-invariant component of the error term, which is the main distinction from fixed effects models that focus on within-entity (e.g., within-individual or within-country) variation by effectively removing entity-specific effects that are constant over time.

Question 20

Panel data differs from cross-section data in that panel data is observed
A)for a number of different individuals in a given time-period.
B)for a given individual in a number of different time-periods.
C)for a given individual in a given time-period.
D)for a number of different individuals in a number of different time-periods.

Accepted Answer

Panel data is characterized by observing multiple subjects (individuals, firms, countries, etc.) over multiple time periods, combining the features of both cross-sectional data (different individuals) and time series data (different time periods).

Quiz 13: Panel Data

Panel data differs from time-series data in that panel data is observed

First-differenced models are preferable to pooled cross-section models because they

The fact that panel data varies across both individuals and time-periods should allow us to ___________ by accounting for the time constant component in the error term.

If there is a pooled cross-section model with and intercept and five explanatory variables and there are 100 observations each year for three years

Fixed-effects models are more appropriate than random-effects models when

One can first-difference panel data in a two-period model by subtracting

Fixed-effects models

Pooled cross-section models

Treating each data point in panel data as an individual observation and not accounting for the fact that individuals are observed over many time periods is referred to as estimating a ____ model.

Fixed-effects models are preferable to pooled cross-section models because they

Fixed-effects models and first-differenced models

Random-effects models improve on pooled cross-section models because they

Pooled cross-section models are not the preferred estimators for panel data models because they

Random-effects models are more appropriate than fixed-effects models when

Fixed-effects models and first-differenced models

Random-effects models are

Fixed-effects models and first-differenced models

Random-effects models

The biggest difference between fixed-effects and random-effects models is that

Panel data differs from cross-section data in that panel data is observed