MC Quiz Answers
Consider the following regression results: Yi_hat = 10 + 2.5 * X1,i + -5.5 * X2,i + 15 * X3,i How much does Yi_hat change, holding all else equal, of increasing X3 by 2 units?
30
Consider the following regression output: Y_hati = 698.9 - 1.50 * X1,i - 0.75 * X2,i You are told that STATA-generated t-statistic on X1's coefficient is -2.56. The standard error for X1's coefficient is approximately a) 0.586 b) 1.96 c) 0.650 d) 0.25
a) 0.586
Assume that you had estimated the following quadratic regression model: Happiness_hat = 607.3 + 3.85 * Income - 0.0423 * Income². If Income increased from 10 to 11 (income is measured in thousands), then the predicted change in Happiness would be (round to two decimals): a) 2.96 b) 3.85 c) cannot be calculated because the function is non-linear d) 3.80
a) 2.96
All of the following are true, with the exception of one condition: a) a high R² always means that an added variable is statistically significant b) a high R² does not mean that there is no omitted variable bias c) a high R² does not mean that the regressors are a true cause of the dependent variable d) a high R² does not necessarily mean that you have the most appropriate set of regressors
a) a high R² always means that an added variable is statistically significant
The difference between an unbalanced and a balanced panel is that a) an unbalanced panel contains missing observations for at least one time period or one entity b) you cannot have both fixed time effects and fixed entity effects regressions c) in the former you may not include drivers who have been drinking in the fatality rate/beer tax study d) the impact of different regressors are roughly the same for balanced but not for unbalanced panels
a) an unbalanced panel contains missing observations for at least one time period or one entity
Autocorrelation of the error terms a) causes the usual OLS standard errors to be inconsistent b) causes OLS to be no longer consistent c) makes it impossible to calculate homoscedasticity only standard errors d) results in OLS being biased
a) causes the usual OLS standard errors to be inconsistent
In time series data, it is useful to think of a randomized controlled experiment a) consisting of the same subject being given different treatments at different points in time b) as being non-existent (this is a time series after all, and there are no real "parallel universes") c) consisting of different subjects being given the same treatment at the same point in time d) consisting of the at least two subjects being given different treatments at the same point in time
a) consisting of the same subject being given different treatments at different points in time
The OLS estimators of the coefficients in multiple regression will have omitted variable bias a) if an omitted determinant of Yi is correlated with at least one of the regressors b) only if an omitted determinant of Yi is a continuous variable c) only if the omitted variable is not normally distributed d) if an omitted variable is correlated with at least one of the regressors, even though it is not a determinant of the dependent variable
a) if an omitted determinant of Yi is correlated with at least one of the regressors
In the simple linear regression model, the regression slope a) indicates by how many units Y changes, given a one unit increase in X b) indicates by how many percent Y changes, given a one percent increase in X c) when multiplied with the explanatory variable will give you the predicted Y d) represents the elasticity of Y on X
a) indicates by how many units Y changes, given a one unit increase in X
In the regression model Yi = β₀ + β₁Xi + β₂Di + β₃(Xi x Di) + ui, where X is a continuous variable and D is a binary variable, β₂ a) indicates the difference in the intercepts of the two regressions b) indicates the difference in the slopes of the two regressions c) is usually positive d) is the difference in means in Y between the two categories
a) indicates the difference in the intercepts of the two regressions
The question of reliability/unreliability of a multiple regression depends on a) internal and external validity b) the quality of your statistical software package c) internal but not external validity d) external but not internal validity
a) internal and external validity
The Times Series Regression with Multiple Predictors a) is the same as the ADL(p,q) with additional predictors and their lags present b) requires that the k regressors and the dependent variable have nonzero, finite eighth moments c) cannot be estimated by OLS due to the presence of multiple lags d) gives you more than one prediction
a) is the same as the ADL(p,q) with additional predictors and their lags present
The analysis is externally valid if a) its inferences and conclusions can be generalized from the population and setting studied to other populations and settings b) some committee outside the author's department has validated the findings c) the study has passed a double blind refereeing process for a journal d) the statistical inferences about causal effects are valid for the population being studied
a) its inferences and conclusions can be generalized from the population and setting studied to other populations and settings
Panel data is also called a) longitudinal data b) time series data c) cross-sectional data d) experimental data
a) longitudinal data
The logit model can be estimated and yields consistent estimates if you are using a) maximum likelihood estimation b) OLS estimation c) differences in means between those individuals with a dependent variable equal to one and those with a dependent variable equal to zero d) the linear probability model
a) maximum likelihood estimation
The OLS estimator is derived by a) minimizing the sum of squared residuals b) minimizing the sum of absolute residuals c) connecting the Yi corresponding to the lowest Xi observation with the Yi corresponding to the highest Xi observation d) making sure that the standard error of the regression equals the standard error of the slope estimator
a) minimizing the sum of squared residuals
If the absolute value of your calculated t-statistic exceeds the critical value from the standard normal distribution you can a) reject the null hypothesis b) safely assume that your regression results are significant c) conclude that most of the actual values are very close to the regression line d) reject the assumption that the error terms are homoskedastic
a) reject the null hypothesis
Misspecification of functional form of the regression function a) results in a type of omitted variable bias b) is more serious in the case of homoscedasticity-only standard error c) is overcome by adding the squares of all explanatory variables d) requires alternative estimation methods such as maximum likelihood
a) results in a type of omitted variable bias
The random walk model is an example of a a) stochastic trend model b) deterministic trend model c) binomial model d) stationary model
a) stochastic trend model
A study based on OLS regressions is internally valid if a) the OLS estimator is unbiased and consistent, and the standard errors are computed in a way that makes confidence intervals have the desired confidence level b) you use a two-sided alternative hypothesis, and standard errors are calculated using the heteroskedasticity-robust formula c) weighted least squares produces similar results, and the t-statistic is normally distributed in large samples d) the errors are homoscedastic, and there are no more than two binary variables present among the regressors
a) the OLS estimator is unbiased and consistent, and the standard errors are computed in a way that makes confidence intervals have the desired confidence level
In the probit regression, the coefficient β₁ indicates a) the change in the z-value associated with a unit change in X b) the change in the probability of Y = 1 given a percent change in X c) the change in the probability of Y = 1 given a unit change in X d) none of the above
a) the change in the z-value associated with a unit change in X
The regression R² is a measure of a) the goodness of fit of your regression line b) whether or not ESS > TSS c) whether or not X causes Y d) the square of the determinant of R
a) the goodness of fit of your regression line
For the polynomial regression model, a) the techniques for estimation and inference developed for multiple regression can be applied b) you can still use OLS estimation techniques, but the t-statistics do not have an asymptotic normal distribution c) the critical values from the normal distribution have to be changed to 1.96², 1.96³, etc. d) you need new estimation techniques since the OLS assumptions do not apply any longer
a) the techniques for estimation and inference developed for multiple regression can be applied
In the probit model Pr(Y=1 | X₁, X₂, ... Xk) = phi(β₀ + β₁X₁ + β₂X₂ + ... + βkXk), a) the β's do not have a simple interpretation b) β₀ is the probability of observing Y when all X's are 0 c) β₀ cannot be negative since probabilities have to lie between 0 and 1 d) the slopes tell you the effect of a unit increase in X on the probability of Y
a) the β's do not have a simple interpretation
When testing joint hypothesis, you should a) use the F-statistics and reject at least one of the underlying hypothesis if the statistic exceeds the critical value b) use t-statistics for each hypothesis and reject the null hypothesis if all of the restrictions fail c) use the F-statistic and reject all the underlying hypothesis if the statistic exceeds the critical value d) use t-statistics for each hypothesis and reject the null hypothesis once the statistic exceeds the critical value for a single hypothesis
a) use the F-statistics and reject at least one of the underlying hypothesis if the statistic exceeds the critical value
Consider the regression example from your textbook, which estimates the effect of beer taxes on fatality rates across the 48 contiguous U.S. states. If beer taxes were set yearly by the national government rather than by the states, then a) you should not use time fixed effects since beer taxes are the same at a point in time across states b) it would not make sense to use state fixed effect c) you can test state fixed effects using homoscedastic-only standard errors d) the OLS estimator will be biased
a) you should not use time fixed effects since beer taxes are the same at a point in time across states
Consider the following regression results: Yi_hat = 10 + 2.5 * X1,i + -5.5 * X2,i + 15 * X3,i What is the predicted value of Yi_hat when X1 = 2, X2 = 4, and X3 = 1? a) 52 b) 8 c) -8 d) -2
b) 8
Consider the estimated equation from a sample of 1000. Yi_hat = 5.25 - 3.75 * Xi (1.75) (2.50) R² = 0.50, SER = 18.6 note: standard errors in parentheses Perform the following hypothesis test: H0: β1 = 0 Ha: β1 ≠ 0 a) Reject the null hypothesis at the 5% level of significance. b) Do not reject the null hypothesis at the 5% level of significance. c) Uncertain - not enough information.
b) Do not reject the null hypothesis at the 5% level of significance.
The following are all least squares assumptions with the exception of: a) Large outliers are unlikely. b) The explanatory variable in regression model is normally distributed. c) (Xi, Yi), i = 1, ..., n are independently and identically distributed. d) The conditional distribution of ui given Xi has a mean of 0.
b) The explanatory variable in regression model is normally distributed.
The interpretation of the slope coefficient in the model Yi = β0 + β1ln(Xi) + ui is as follows: a) a change in X by one unit is associated with a β1 change in Y b) a 1% change in X is associated with a change in Y of 0.01β1 c) a change in X by one unit is associated with a β1 100% change in Y d) a 1% change in X is associated with a β1% change in Y
b) a 1% change in X is associated with a change in Y of 0.01β1
The Cochrane-Orcutt iterative method is a) a method to compute HAC standard errors b) a special case of GLS estimation c) a grid search for the autoregressive parameters on the error process d) a special case of maximum likelihood estimation
b) a special case of GLS estimation
Simultaneous causality bias a) is also called sample selection bias b) arises in a regression of Y on X when, in addition to the causal link of interest from X to Y, there is a causal link from Y to X c) results in biased estimators if there is heteroskedasticity in the error term d) happens in complicated systems of equations called block recursive systems
b) arises in a regression of Y on X when, in addition to the causal link of interest from X to Y, there is a causal link from Y to X
The OLS residuals in the multiple regression model a) are typically the same as the population regression function errors b) can be calculated by subtracting the fitted values from the actual values c) are zero because the predicted values are another name for forecasted values d) cannot be calculated because there is more than one explanatory variable
b) can be calculated by subtracting the fitted values from the actual values
The confidence interval for the sample regression function slope a) can be used to compare the value of the slope relative to that of the intercept b) can be used to conduct a test about a hypothesized population regression function slope c) allows you to make statements about the economic importance of your estimate d) adds and subtracts 1.96 from the slope
b) can be used to conduct a test about a hypothesized population regression function slope
A survey of earnings contains an unusually high fraction of individuals who state their weekly earnings in 100s, such as 300, 400, 500, etc. This is an example of a) simultaneous causality bias b) errors-in-variables bias c) companies that typically bargain with workers in 100s of dollars d) sample selection bias
b) errors-in-variables bias
Time Fixed Effects regression are useful in dealing with omitted variables a) when there are more than 100 observations b) if these omitted variables are constant across entities but not over time c) if these omitted variables vary across entities and not over time d) even if you only have a cross-section of data available
b) if these omitted variables are constant across entities but not over time
Finding a small value of the p-value (e.g. less than 5%) a) implies that the t-statistic is less than 1.96 b) indicates evidence against the null hypothesis c) will only happen roughly one in twenty samples d) indicates evidence in favor of the null hypothesis
b) indicates evidence against the null hypothesis
Autoregressive distributed lag models include a) current and lagged values of the residuals b) lags of the dependent variable and lagged values of an additional predictor variable c) current and lagged values of the error term d) lags and leads of the dependent variable
b) lags of the dependent variable and lagged values of an additional predictor variable
Consider the multiple regression model with two regressors X1 and X2, where both variables are determinants of the dependent variable. You first regress Y on X1 only and find no relationship. However when regressing Y on X1 and X2, the slope coefficient β1 changes by a large amount. This suggests that your first regression suffers from a) perfect multicolinearity b) omitted variable bias c) heteroskedasticity d) dummy variable trap
b) omitted variable bias
Stationarity means that the a) forecasts remain within 1.96 standard deviations outside the sample period b) probability distribution of the time series variable does not change over time c) times series has a unit root d) error terms are not correlated
b) probability distribution of the time series variable does not change over time
If the absolute value of your calculated t-statistic exceeds the critical value from the standard normal distribution, you can a) safely assume that your regression results are insignificant b) reject the null hypothesis c) reject the assumption that the error terms are homoscedastic d) conclude that most of the actual values are very close to the regression line
b) reject the null hypothesis
The interpretation of the coefficients in a distributed lag regression as causal dynamic effects hinges on a) using GLS rather than OLS b) the assumption that X is exogenous c) not having more than four lags when using quarterly data d) the use of monthly rather than annual data
b) the assumption that X is exogenous
If the estimates of the coefficients of interest change substantially across specifications, a) then choose the specification for which your coefficient of interest is most significant b) then this often provides evidence that the original specification had omitted variable bias c) then this can be expected from sample variation d) then you should change the scale of the variables to make the changes appear to be smaller
b) then this often provides evidence that the original specification had omitted variable bias
Under imperfect multicolinearity a) the error terms are highly, but not perfectly, correlated b) two or more of the regressors are highly correlated c) the OLS estimator is biased even in samples of n > 100 d) the OLS estimator cannot be computed
b) two or more of the regressors are highly correlated
Ascertaining whether or not a regressor is strictly exogenous or exogenous ultimately requires all of the following with the exception of a) economic theory b) use of HAC standard errors c) expert judgment d) institutional knowledge
b) use of HAC standard errors
Consider a panel regression of unemployment rates for the G7 countries (United States, Canada, France, Germany, Italy, United Kingdom, Japan) on a set of explanatory variables for the time period 1980-2000 (annual data). If you included entity and time fixed effects, you would need to specify the following number of binary variables: a) 6 b) 21 c) 26 d) 28
c) 26
The error term is homoscedastic if a) Xi is normally distributed b) there are no outliers c) Var[ ui | Xi ] is constant for all i = 1, 2, ..., n d) Var[ ui | Xi] depends on Xi
c) Var[ ui | Xi ] is constant for all i = 1, 2, ..., n
An example of a quadratic regression model is a) Yi = β₀ + β₁X + β₂Y² + ui b) Yi = β₀ + β₁X + c) Yi = β₀ + β₁X + β₂X² + ui
c) Yi = β₀ + β₁X + β₂X² + ui
The main advantage of using panel data over cross sectional data is that it a) allows you to analyze behavior across time but not across entities b) allows you to look up critical values in the standard normal distribution c) allows you to control for some types of omitted variables without actually observing them d) gives you more observations
c) allows you to control for some types of omitted variables without actually observing them
The first difference of the logarithm of Yt equals a) the first different of Y b) the growth rate of Y exactly c) approximately the growth rate of Y when the growth rate is small d) the difference between the lead and the lag of Y
c) approximately the growth rate of Y when the growth rate is small
The OLS residuals a) can be calculated using the errors from the regression function b) are unknown since we do not know the population regression function c) can be calculated by subtracting the fitted values from the actual values d) should not be used in practice since they indicate that your regression does not run through all your observations
c) can be calculated by subtracting the fitted values from the actual values
A distributed lag regression a) can also be used with cross-sectional data b) is sometimes referred to as ADL c) gives estimates of dynamic causal effects d) is also called AR(p)
c) gives estimates of dynamic causal effects
The Augmented Dickey Fuller (ADF) t-statistic a) has a normal distribution in large samples b) is a two-sided test c) is an extension of the Dickey-Fuller test when the underlying model is AR(p) rather than AR(1) d) has the identical distribution whether or not a trend is included or not
c) is an extension of the Dickey-Fuller test when the underlying model is AR(p) rather than AR(1)
The construction of the t-statistic for a one- and a two-sided hypothesis a) uses ±1.96 for the two-sided test, but only +1.96 for the one-sided test b) depends on the critical value from the appropriate distribution c) is the same d) is different since the critical value must be 1.645 for the one-sided hypothesis, but 1.96 for the two-sided hypothesis (using a 5% probability for the Type I error)
c) is the same
The concept of exogeneity is important because a) under strict exogeneity, OLS may not be efficient as an estimator of dynamic causal effects b) maximum likelihood estimation is no longer valid c) it clarifies whether or not the variable is determined inside or outside your model d) endogenous variables are not stationary, but exogenous variables are
c) it clarifies whether or not the variable is determined inside or outside your model
A possible solution to errors-in-variables bias is to a) use the square root of that variable since the error becomes smaller b) choose different functional forms c) mitigate the problem through instrumental variables regression d) use log-log specifications
c) mitigate the problem through instrumental variables regression
Multiplying the dependent variable by 100 and the explanatory variable by 100,000 leaves the a) OLS estimate of the slope the same b) OLS estimate of the intercept the same c) regression R-squared the same d) variance of the OLS estimators the same
c) regression R-squared the same
The following tools from multiple regression analysis carry over in a meaningful manner to the linear probability model, with the exception of the a) 95% confidence interval using ±1.96 times the standard error b) joint hypothesis testing c) regression R² d) significance test using the t-statistic
c) regression R²
The AR(p) model a) can be represented as follows: Yt = β₀ + β₁X₁ + βpYt-p + ut b) is defined as Yt = β₀ + βpYt-p + ut c) represents Yt as a linear function of p of its lagged values d) can be written as Yt = β₀ + β₁Yt-1 + ut-p
c) represents Yt as a linear function of p of its lagged values
When you add state fixed effects to a simple regression model for U.S. states over a certain time period, and the regression R² increases significantly, then it is safe to assume that a) the included explanatory variables, other than the state fixed effects, are unimportant b) time fixed effects are unimportant c) state fixed effects account for a large amount of the variation in the data d) the coefficients on the other included explanatory variables will not change
c) state fixed effects account for a large amount of the variation in the data
If you wanted to test, using a 5% significance level, whether or not a specific slope coefficient is equal to one, then you should (assuming large sample) a) add and subtract 1.96 from the slope and check if that interval includes 1 b) see if the slope coefficient is between 0.95 and 1.05 c) subtract 1 from the estimated coefficient, divide the difference by the standard error, and check if the resulting ratio is larger than 1.96 d) check if the adjusted R² is close to 1
c) subtract 1 from the estimated coefficient, divide the difference by the standard error, and check if the resulting ratio is larger than 1.96
To decide whether Yi = β0 + β1X + ui or ln(Yi) = β0 + β1X + ui fits the data better, you cannot consult the regression R² because a) the slope no longer indicates the effect of a unit change of X on Y in the log-linear model b) ln(Y) may be negative for 0 < Y < 1 c) the TSS are different d) the regression R² can be greater than one in the second model
c) the TSS are different
In the linear probability model, the interpretation of the slope coefficient is a) the response in the dependent variable to a percentage change in the regressor b) the change in odds associated with a unit change in X, holding other regressors constant c) the change in probability that Y = 1 associated with a unit change in X, holding other regressors constant d) not all that meaningful since the dependent variable is either 0 or 1
c) the change in probability that Y = 1 associated with a unit change in X, holding other regressors constant
In the log-log model, the slope coefficient indicates a) ΔY / ΔX b) ??? c) the elasticity of Y with respect to X d) the effect that a unit change in X has on Y
c) the elasticity of Y with respect to X
You should use the QLR test for breaks in the regression coefficients when a) the suspected break date is known b) there are breaks in only some, but not all, of the regression coefficients c) the suspected break date is not known d) the Chow F-test has a p value of between 0.05 and 0.10
c) the suspected break date is not known
The distributed lag model assumptions include all of the following with the exception of: a) There is no perfect multicolinearity. b) The random variables Xt and Yt have a stationary distribution. c) E(ut | Xt, Xt-1, Xt-2) = 0 d) Xt is strictly exogenous
d) Xt is strictly exogenous
An example of the interaction term between two independent, continuous variables is (using the notation from Stock and Watson) a) Yi = β0 + β1D1i + β2D2i + β3(D1i * D2i) + ui b) Yi = β0 + β1X1i + β2X2i + ui c) Yi = β0 + β1Xi + β2Di + β3(Xi * Di) + ui d) Yi = β0 + β1X1i + β2X2i + β3(X1i * X2i) + ui
d) Yi = β0 + β1X1i + β2X2i + β3(X1i * X2i) + ui
You extract approximately 5,000 observations from the Current Population Survey (CPS) and estimate the following regression function: ahei_hat = 3.32 - 0.45 * Agei (1.00) (0.04) R² = 0.02, SER = 8.66 note: standard errors in parentheses where ahe is average hourly earnings and age is the individual's age. Given the specification, your 95% confidence interval for β1: a) [-0.25, -0.05] b) [-0.63, -0.40] c) cannot be determined given the information provided d) [-0.53, -0.37]
d) [-0.53, -0.37]
If you reject a joint null hypothesis using the F-test in a multiple hypothesis setting, then a) the regression is always significant b) all of the hypotheses are always simultaneously rejected c) the F-statistic must be negative d) a series of t-tests may or may not give you the same conclusion
d) a series of t-tests may or may not give you the same conclusion
In multiple regression, the R² can increase whenever a regressor is a) greater than 1.96 in absolute value b) added unless there is heteroskedasticity c) added unless there is multicolinearity d) added
d) added
The true causal effect might not be the same in the population studied and the population of interest because a) the study is out of date b) of geographical differences c) of differences in characteristics of the population d) all of the above
d) all of the above
HAC standard errors and clustered standard errors are related as follows: a) clustered standard errors are the square root of HAC standard errors b) they are the same c) they are the same if the data is differenced d) clustered standard errors are one type of HAC standard error
d) clustered standard errors are one type of HAC standard error
The probit model a) is the same as the logit model b) always gives the same fit for the predicted values as the linear probability model for values between 0.1 and 0.9 c) should not be used since it is too complicated d) forces the predicted values to lie between 0 and 1
d) forces the predicted values to lie between 0 and 1
In a linear probability model, a predicted value of 0.6 means that a) the model makes little sense, since the dependent variable can only be 0 or 1 b) given the values for the explanatory variables, there is a 40 percent probability that the dependent variables will equal one c) the most likely value the dependent variable will take on is 60 percent d) given the values for the explanatory variables, there is a 60 percent probability that the dependent variable will equal one
d) given the values for the explanatory variables, there is a 60 percent probability that the dependent variable will equal one
The long-run cumulative dynamic multiplier a) is the difference between the coefficient on Xt-1 and Xt-r b) is the coefficient on Xt-r in the standard formulation of the distributed lag model c) cannot be calculated since in the long-run, we are all dead d) is the sum of all individual dynamic multipliers
d) is the sum of all individual dynamic multipliers
Simultaneous causality a) means you must run a second regression of X on Y b) cannot be established since regression analysis only detects correlation between variables c) means that a third variable affects both X and Y d) leads to correlation between the regressor and the error term
d) leads to correlation between the regressor and the error term
The dummy variable trap is an example of a) something that does not happen to university or college students b) imperfect multicolinearity c) something that is of theoretical interest only d) perfect multicolinearity
d) perfect multicolinearity
Heteroskedasticity- and autocorrelation-consistent standard errors a) are calculated when using the Cochrane-Orcutt iterative procedure b) require an estimation strategy different than OLS c) have the same formula as the heteroskedasticity robust standard errors in cross-sections d) should be used when errors are autocorrelated
d) should be used when errors are autocorrelated
The linear probability model is a) the application of the multiple regression model with a continuous left-hand side variable and a binary variable as at least one of the regressors b) another word for logit estimation c) an example of probit estimation d) the application of the linear multiple regression model to a binary dependent variable
d) the application of the linear multiple regression model to a binary dependent variable
Sample selection bias occurs when a) data are collected from a population by simple random sampling b) samples are chosen to be small rather than large c) the choice between two samples is made by the researcher d) the availability of the data is influenced by a selection process that is related to the value of the dependent variable
d) the availability of the data is influenced by a selection process that is related to the value of the dependent variable
E(ui | Xi) = 0 says that a) the sample mean of the Xs is much larger than the sample mean of the errors b) the sample regression function residuals are unrelated to the explanatory variable c) dividing the error by the explanatory variable results in a zero (on average) d) the conditional distribution of the error given the explanatory variable has a zero mean
d) the conditional distribution of the error given the explanatory variable has a zero mean
Using the textbook example of 420 California school districts and the regression of testscores on the student-teacher ratio, you find that the standard error on the slope coefficient is 0.51 when using the heteroskedasticity robust formula, while it is 0.48 when employing the homoscedasticity only formula. When calculating the t-statistic, the recommended procedure is to: a) use the homoscedasticity only formula because the t-statistic becomes larger b) make a decision depending on how much different the estimate of the slope is under the two procedures c) first test for homoscedasticity of the errors and then make a decision d) use the heteroskedasticity robust formula
d) use the heteroskedasticity robust formula
The BIC is a statistic a) only used in cross-sectional analysis b) developed by the Bank of England in its river of blood analysis c) commonly used to test for serial correlation d) used to help the researcher choose the number of lags in an autoregression
d) used to help the researcher choose the number of lags in an autoregression
The Granger Causality Test a) is a special case of the Augmented Dickey-Fuller test b) is a rather complicated test for statistical independence c) establishes the direction of causality (as used in common parlance) between X and Y in addition to correlation d) uses the F-statistic to test the hypothesis that certain regressors have no predictive content for the dependent variable beyond that contained in the other regressors
d) uses the F-statistic to test the hypothesis that certain regressors have no predictive content for the dependent variable beyond that contained in the other regressors
In a two regressor regression model, if both are determinants of Y and you exclude one then a) the remaining coefficient is certainly biased b) it is no longer reasonable to assume that the errors are heteroskedastic c) the OLS estimator no longer exists d) you are no longer controlling for the influence of the omitted variable
d) you are no longer controlling for the influence of the omitted variable
