Examlex
Your textbook reports the following result from an two-way fixed effects (entity and time fixed effects)regression model: = -0.66 BeerTax + StateFixedEffects + TimeFixedEffects
(0.36)
Where the number in parenthesis is the heteroskedasticity- and autocorrelation-consistent (HAC)standard error.
a. Calculate the t-statistic. Can you reject the null hypothesis that the slope coefficient is zero in the population, using a two-sided test and a 5% significance level?
b. Given that economic theory suggests that the population slope is negative under the alternative hypothesis, is it possible to use a one-sided test here? In that case, does your conclusion change?
c. Using only heteroskedasticity-robust standard errors, but not HAC standard errors, the value in parenthesis becomes 0.25. Repeat the calculations in (a)and report your decision based on a two-sided test.
d. Since the coefficient becomes more statistically significant in (d), should this influence your choice of standard errors? Why or why not?
Division Manager
An individual responsible for overseeing a specific division within a company, ensuring its operational and financial performance.
Residual Income
Income that remains after deducting all costs, including the cost of capital, from business or investment earnings.
Minimum Return
The lowest acceptable rate of return on an investment that a manager or investor is willing to accept, considering the risk and capital involved.
Profit Margin Component
A financial metric that measures the amount of net income generated as a percentage of revenue, indicating the efficiency of a company in converting sales into actual profit.
Q5: Using the model Y = Xβ
Q10: Consider the regression model Y<sub>i</sub> =
Q11: Consider a competitive market where the demand
Q11: Your textbook discussed the regression model
Q13: In the context of a controlled
Q16: A statistical analysis is internally valid if<br>A)its
Q23: (Requires Advanced material)Nonlinear least squares estimators in
Q31: Your textbook presents as an example of
Q50: Consider a regression with two variables, in
Q60: The following are all least squares assumptions