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About 833 results. 1294 free access solutions
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| m79738 | We used the data in MEAP93.RAW for Example 2.12. Now we want to explore the relationship between the math pass rate (math10) and spending per student (expend).
(i) Do you think each additional dollar spent has the same effect on the pass rate, or does a diminishing effect seem more appropriate? Explain?
(ii) In the population model
math10 = (0 + (1 log (expend) + u.
argue that (1/10 is the percentage point change in math10 given a 10% increase in expend.
(iii) Use the data in MEAP93.RAW to estimate the model from part (ii). Report the estimated equation in the usual way, including the sample size and R-squared.
(iv) How big is the estimated spending effect? Namely, if spending increases by 10%, what is the estimated percentage point increase in math10?
(v) One might worry that regression analysis can produce fitted values fro math10 that are greater than 100. Why is this not much of a worry in this data set? |
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| m79801 | When atndrte2 and ACT. atndrte are added to the equation estimated in (6.19), the R-squared becomes .232. Are these additional terms jointly significant at the 10% level? Would you include them in the model? |
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| m79810 | When the errors in a regression model have AR(1) serial correlation, why do the OLS standard errors tend to underestimate the sampling variation in the j? Is it always true that the OLS standard errors are too small? |
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| m79811 | When the three event indicators beftle6, qffile6, and afdec6 are dropped from equation (10.22), we obtain R2 = .281 and = .264. Are the event indicators jointly significant at the 10% level? |
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| m79812 | When the three event indicators beftle6, qffile6, and afdec6 are dropped from equation (10.22), we obtain R2 = .281 and = .264. Are the event indicators jointly significant at the 10% level? |
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| m79827 | Which of the following are consequences of heteroskedasticity?
(i) The OLS estimators, j, are inconsistent.
(ii) The usual F statistic no longer has an F distribution.
(iii) The OLS estimators are no longer BLUE. |
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| m79828 | Which of the following can cause OLS estimators to be biased?
(i) Heteroskedasticity.
(ii) Omitting and important variable.
(iii) A sample correlation coefficient of .95 between two independent variables both included in the model? |
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| m79829 | Which of the following can cause the usual OLS t statistics to be invalid (that is, not to have t distributions under H0)?
(i) Heteroskedasticity.
(ii) A sample correlation coefficient of .95 between two independent variables that are in the model.
(iii) Omitting an important explanatory variables. |
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| m79835 | Why can we not use first differences when we have independent cross sections in two years (as opposed to panel data)? |
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| m79844 | With a single explanatory variable, the equation used to obtain the between estimator is
where the overbar represents the average over time. We can assume that E(a.) = 0 because we have included an intercept in the equation. Suppose that i. is uncorrelated with i, but Cov(xit, ai) = σxa for all t (and i because of random sampling in the cross section).
(i) Letting/3, be the between estimator, that is, the OLS estimator using the time averages, show that
where the probability limit is defined as N → ∞.
(ii) Assume further that the xit, for all t = 1, 2,..., T, are uncorrelated with constant variance σ2x. Show that plim 1 = β1 + T (σxn/ σ2x).
(iii) If the explanatory variables are not very highly correlated across time, what does part (ii) suggest about whether the inconsistency in the between estimator is smaller when there are more time periods? |
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| m79886 | Write a two-equation system in "supply and demand form," that is, with the same variable y1 (typically, "quantity") appearing on the left-hand side:
(i) If α, = 0 or α2 = 0, explain why a reduced form exists for y1, (Remember, a reduced form expresses y, as a linear function of the exogenous variables and the structural errors.) If α1 ≠ 0 and α2 = 0, find the reduced form for y2.
(ii) If α1 ≠ 0, α2 ≠ 0, and α, ≠ α2, find the reduced form for y1. Does y2 have a reduced form in this case?
(iii) Is the condition α1 ≠ α2 likely to be met in supply and demand examples? Explain. |
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| m79947 | You need to use two data sets for this exercise, JTRAIN2.RAW and JTRAIN3.RAW. The former is the outcome of a job training experiment. The file JTRAIN3.RAW contains observational data, where individuals themselves largely determine whether they participate in job training. The data sets cover the same time period.
(i) In the data set JTRAIN2.RAW, what fraction of the men received job training? What is the fraction in JTRAIN3.RAW? Why do you think there is such a big difference?
(ii) Using JTRAIN2.RAW, run a simple regression of re78 on train. What is the estimated effect of participating in job training on real earnings?
(iii) Now add as controls to the regression in part (ii) the variables re74, re75, educ, age, black, and hisp. Does the estimated effect of job training on re78 change much? How come? (Hint: Remember that these are experimental data.)
(iv) Do the regressions in parts (ii) and (iii) using the data in JTRAIN3.RAW, reporting only the estimated coefficients on train, along with their / statistics. What is the effect now of controlling for the extra factors, and why?
(v) Define avgre = (re74 + re75)/2. Find the sample averages, standard deviations, and minimum and maximum values in the two data sets. Are these data sets representative of the same populations in 1978?
(vi) Almost 96% of men in the data set JTRAIN2.RAW have avgre less than $10,000. Using only these men, run the regression
re78 on train,re74,re75,educ,age,black,hisp
and report the trai |
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| m96844 | Suppose that y has the pdf f (y | x) = (1/x β)e−y/(β x), y > 0. Then E[y | x] = β x and Var[y | x] = (β x)2. For this model, prove that GLS and MLE are the same, even though this distribution involves the same parameters in the conditional mean function and the disturbance variance. |
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