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About 833 results. 1294 free access solutions
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| m79532 | Use the data set in WAGE2.RAW for this problem. As usual, be sure all of the following regressions contain an intercept. |
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| m79533 | Use the data set WAGE2.RAW for this exercise.
(i) Use the variable KWW (the "knowledge of the world of work" test score) as a proxy for ability in place of IQ in Example 9.3. What is the estimated return to education in this case?
(ii) Now, use IQ and KWW together as proxy variables. What happens to the estimated return to education?
(iii) In part (ii), are IQ and KWW individually significant? Are they jointly significant? |
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| m79540 | Use the Economic Report of the President (2005 or later) to update the data in CONSUMP.RAW, at least through 2003. Reestimate equation (16.35). Do any important conclusions change? |
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| m79541 | Use the entire panel data set in AIRFARE.RAW for this exercise. The demand equation in a simultaneous equations unobserved effects model is
Log(passenit) = θit + α1 log(fareit) + ait + uit,
where we absorb the distance variables into ait.
(i) Estimate the demand function using fixed effects, being sure to include year dummies to account for the different intercepts. What is the estimated elasticity?
(ii) Use fixed effects to estimate the reduced form
Log(fareit) θt2 + π21 concenit + ai2 + vit2,
Perform the appropriate test to ensure that concenit, can be used as an IV for log(fareit).
(iii) Now estimate the demand function using the fixed effects transformation along with IV, as in equation (16.42). Now what is the estimated elasticity? Is it statistically significant? |
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| m79545 | [Use the fact that the j solve the first order conditions in (3.13), and the j must solve the first order conditions involving the rescaled dependent and independent variables.] |
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| m79563 | Use the housing price data in HPRICE1.RAW for this exercise.
(i) Estimate the model
log(price) = (0 + (1 log(lotsize) + (2 log(sqrft) + (3 bdrms + u
and report the result in the usual OLS format.
(ii) Find the predicated value of log(price), when lotsize = 20,000, sqrft = 2,500, and bdrms = 4. Using the methods in Section 6.4, find the predicated value of price at the same values of the explanatory variables.
(iii) For explaining variation in price, decide whether you prefer the model from part (i) or the model
price = (0 + ((1 lotsize + (2 sqrft + (3 bdrms + u. |
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| m79579 | Use the MROZ.RAW data for this exercise.
(i) Using the 428 women who were in the workforce, estimate the return to education by OLS including exper, exper2, nwifeinc, age, kidslt6, and kidsge6 as explanatory variables. Report your estimate on educ and its standard error.
(ii) Now, estimate the return to education by Heckit, where all exogenous variables show up in the second-stage regression. In other words, the regression is log(wage) on educ, exper, exper2, nwifeinc, age, kidslt6, kidsge6, and A. Compare the estimated return to education and its standard error to that from part (i).
(iii) Using only the 428 observations for working women, regress λ on educ, exper, exper2, nwifeinc, age, kidslt6, and kidsge6. How big is the /?-squared? How does this help explain your findings from part (ii)? |
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| m79598 | Use the state-level data on murder rates and executions in MURDER.RAW for the following exercise.
(i) Consider the unobserved effects model
where 6, simply denotes different year intercepts and a is the unobserved state effect. If past executions of convicted murderers have a deterrent effect, what should be the sign of β1? What sign do you think β2 should have? Explain.
(ii) Using just the years 1990 and 1993, estimate the equation from part (i) by pooled OLS. Ignore the serial correlation problem in the composite errors. Do you find any evidence for a deterrent effect?
(iii) Now, using 1990 and 1993, estimate the equation by fixed effects. You may use first differencing since you are only using two years of data. Now, is there evidence of a deterrent effect? How strong?
(iv) Compute the heteroskedasticity-robust standard error for the estimation in part (iii). It will be easiest to use first differencing.
(v) Find the state that has the largest number for the execution variable in 1993. (The variable exec is total executions in 1991, 1992, and 1993.) How much bigger is this value than the next highest value?
(vi) Estimate the equation using first differencing, dropping Texas from the analysis. Compute the usual and heteroskedasticity-robust standard errors. Now, what do you find? What is going on?
(vii) Use all three years of data and estimate the model by fixed effects. Include Texas in the analysis. Discuss the size and statistical significance of the dete |
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| m79599 | Use the subset of 401KSUBS.RAW with fsize = 1; this restricts the analysis to single person households; see also Computer Exercise C4.8.
(i) What is the youngest age of people in this sample? How many people are at that age?
(ii) In the model
nettfa = (0 + (1 jnc + (2 age + (3 age2 + u,
what is the literal interpretation of (2? By itself, is it of much interest?
(iii) Estimate the model from part (ii) and report the results in standard form. Are you concerned that the coefficient on age is negative? Explain.
(iv) Because the youngest people in the sample are 25, it makes sense to think that, for a given level of income, the lowest average amount of net total financial assets is at age 25. Recall that the partial effect of age on nettfa is (2 + 2( age, so the partial effect at age 25 is (2 + 2(3,(25) = (2 + 50(3; call this (2 = 0. Find 2 and obtain the two sided p-value for testing H0: (2 = 0. You should conclude that 62 is small and very statistically insignificant. [One way to do this is to estimate the model nettfa = a0 + (1 inc + (2 age + (3 (age - 25)2 + u, where the intercept, a0, is different from (0. There are other ways, too.
(v) Because the evidence against H0: (2 = 0 is very weak, set it to zero and estimate the model
nettfa = a0 + (1inc + (3 (age - 25 )2 + u.
In terms of goodness-of-fit, does this model fit better than that in part (ii)?
(vi) For the estimated equation in part (v), set inc = 30 (roughly, the average value) and graph the relati |
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| m79613 | Use VOTE 1.RAW for this exercise.
(i) Estimate a model with voteA as the dependent variable and prtystrA, democA, log(expendA), and log(expendB) as independent variables. Obtain the OLS residuals, u, and regress these on all of the independent variables. Explain why you obtain R2 = 0.
(ii) Now, compute the Breusch-Pagan test for heteroskedasticity. Use the F statistic version and report the p-value.
(iii) Compute the special case of the White test for heteroskedasticity, again using the F statistic form. How strong is the evidence for heteroskedasticity now? |
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| m79632 | Using data from 1988 for houses sold in Andover, Massachusetts, from Kiel and McClain (1995), the following equation relates housing price (price) to the distance from a recently built garbage incinerator (dist):
n = 135, R2 = 0.162.
(i) Interpret the coefficient on log (dist). Is the sign of this estimate what you expect it to be?
(ii) Do you think simple regression provides an unbiased estimator of the ceteris paribus elasticity of price with respect to dist? (Think about the city s decision on where to put the incinerator).
(iii) What other factors about a house affect its price? Might these be correlated with distance from the incinerator? |
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| m79643 | Using the data in GPA2.RAW on 4,137 college students, the following equation was estimated by OLS:
Where colgpa is measured on a four-point scale, hsperc s the percentile in the high school graduating class (defined so that, for example, hsperc = 5 means the top 5% of the calss),
(i) Why does it make sense for the coefficient on hsperc to be negative?
(ii) What is the predicted college GPA when hsperc = 20 and sat = 1,050?
(iii) Suppose that two high school graduates, A and B, graduated in the same percentile from high school, but Student A s SAT score was 140 points higher (about one standard deviation in the sample). What is the predicted difference in college GPA for these two students? Is the difference large?
(iv) Holding hsperc fixed, what difference in SAT scores leads to a predicted colgpa difference of .50, or one half of a grade point? Comment on your answer? |
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| m79644 | Using the data in GPA2.RAW, the following equation was estimated:
The variable sat is the combined SAT score, hsize is size of the student s high school graduating class, in hundreds, female is a gender dummy variable, and black is a race dummy variable equal to one for blacks and zero otherwise.
(i) Is there strong evidence that hsize2 should be included in the model? From this equation, what is the optimal high school size?
(ii) Holding hsize fixed, what is the estimated difference in SAT score between nonblack females and nonblack males? How statistically significant is this estimated difference?
(iii) What is the estimated difference in SAT score between nonblack males and black males? Test the null hypothesis that there is no difference between their scores, against the alternative that there is a difference.
(iv) What is the estimated difference in SAT score between black females and nonblack females? What would you need to do to test whether the difference is statistically significant? |
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| m79645 | Using the data in GPA3.RAW, the following equation was estimated for the fall and second semester students:
Here, trmgpa is term GPA, crsgpa is a weighted average of overall GPA in courses taken, cumgpa is GPA prior to the current semester, tothrs is total credit hours prior to the semester, sat is SAT score, hsperc is graduating percentile in high school class, female is a gender dummy, and season is a dummy variable equal to unity if the student s sport is in season during the fall. The usual and heteroskedasticity robust standard errors are reported in parentheses and brackets, respectively.
(i) Do the variables crsgpa, cumgpa, and tothrs have the expected estimated effects? Which of these variables are statistically significant at the 5% level? Does it matter which standard errors are used?
(ii) Why does the hypothesis H0: (crsgpa - 1 make sense? Test this hypothesis against the two-sided alternative at the 5% level, using both standard errors. Describe your conclusions.
(iii) Test whether there is an in-season effect on term GPA, using both standard errors. Does the significance level at which the null can be rejected depend on the standard error used? |
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| m79646 | Using the data in KIELMC.RAW, the following equations were estimated using the years 1978 and 1981:
And
Compare the estimates on the interaction term y81-nearinc with those from equation (13.9). Why are the estimates so different? |
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| m79647 | Using the data in RDCHEM.RAW, the following equation was obtained by OLS:
(i) At what point does the marginal effect of sales on rdintens become negative?
(ii) Would you keep the quadratic term in the model? Explain.
(iii) Define salesbil as sales measured in billions of dollars: salesbil = sales/1,000. Rewrite the estimated equation with salesbil and salesbil2 as the independent variables. Be sure to report standard errors and the R-squared. [salesbil2 / (1,000)2.]
(iv) For the purpose of reporting the results, which equation do you prefer? |
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| m79648 | Using the data in SLEEP75.RAW (see also Problem 3.3), we obtain the estimated equation
The variable sleep is total minutes per week spent sleeping at night, totwrk is total weekly minutes spent working, educ and age are measured in years, and male is a gender dummy.
(i) All other factors being equal, is there evidence that men sleep more than women? How strong is the evidence?
(ii) Is there a statistically significant tradeoff between working and sleeping? What is the estimated tradeoff?
(iii) What other regression do you need to run to test the null hypothesis that, holding other factors fixed, age has no effect on sleeping? |
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| m79666 | Using the monthly data in VOLAT.RAW, the following model was estimated:
where pcip is the percentage change in monthly industrial production, at an annualized rate, and pcsp is the percentage change in the Standard & Poor s 500 Index, also at an annualized rate.
(i) If the past three months of pcip are zero and pcsp-1 = 0, what is the predicted growth in industrial production for this month? Is it statistically different from zero?
(ii) If the past three months of pcip are zero but pcsp-1 = 10, what is the predicted growth in industrial production?
(iii) What do you conclude about the effects of the stock market on real economic activity? |
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| m79686 | (v) Do the algebra to simplify the expression in part (iv) to equation. |
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| m79710 | VOTE2.RAW includes panel data on House of Representative elections in 1988 and 1990. Only winners from 1988 who are also running in 1990 appear in the sample: these are the incumbents. An unobserved effects model explaining the share of the incumbent s vote in terms of expenditures by both candidates is
voteit = (0 + (0d90t + (1log(inexpit) + (2 log(chexpit) + (3 incshrit + ai + uit
where incshrit is the incumbent s share of total campaign spending (in percentage form). The unobserved effect a. contains characteristics of the incumbent-such as "quality"-as well as things about the district that are constant. The incumbent s gender and party are constant over time, so these are subsumed in ai. We are interested in the effect of campaign expenditures on election outcomes.
(i) Difference the given equation across the two years and estimate the differenced equation by OLS. Which variables are individually significant at the 5% level against a two-sided alternative?
(ii) In the equation from part (i), test for joint significance of (log(inexp) and (log(chexp). Report the p-value.
(iii) Reestimate the equation from part (i) using Aincshr as the only independent variable. Interpret the coefficient on Mncshr. For example, if the incumbent s share of spending increases by 10 percentage points, how is this predicted to affect the incumbent s share of the vote?
(iv) Redo part (iii), but now use only the pairs that have repeat challengers. [This allows us to control for char |
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